The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index; none of the 17 sources its claims cite is checked for support yet.
A calibration hierarchy for risk models was defined: from utopia to empirical data
Open accessAbstract
Objective Calibrated risk models are vital for valid decision support. We define four levels of calibration and describe implications for model development and external validation of predictions.Study design and setting We present results based on simulated data sets.Results A common definition of calibration is "having an event rate of R% among patients with a predicted risk of R%," which we refer to as "moderate calibration." Weaker forms of calibration only require the average predicted risk (mean calibration) or the average prediction effects (weak calibration) to be correct. "Strong calibration" requires that the event rate equals the predicted risk for every covariate pattern. This implies that the model is fully correct for the validation setting. We argue that this is unrealistic: the model type may be incorrect, the linear predictor is only asymptotically unbiased, and all nonlinear and interaction effects should be correctly modeled. In addition, we prove that moderate calibration guarantees nonharmful decision making. Finally, results indicate that a flexible assessment of calibration in small validation data sets is problematic.Conclusion Strong calibration is desirable for individualized decision support but unrealistic and counter productive by stimulating the development of overly complex models. Model development and external validation should focus on moderate calibration.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
0 of 17 cited sources checked · 0 supported · 0 flagged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There was some miscalibration in mean risk for Examples 1-2 where the development sample was small, even though the correct underlying model formulation was estimated.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 3.1. Level 1: mean calibration (Calibration-in-the-large)
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Enlarging the development dataset alleviated the problems (Example 4 in Table 1, Figure 1D).”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 2.2. Illustration: Examples 1-5 For illustration, we consider five simulated examples, as previously presented [5].We randomly generate four independent predictor variables to .These predictor variables are ordinal with three categories (-1, 0, and 1) that each have 33% prevalence in order to visualize calibration by covariate pattern.Let outcome be generated by an underlying logistic regression model with the true linear predictor , hence the intercept equals 0, and to are assumed to have linear main effects [5].In simulations, the true probability of event is calculated using the linear predictor and the observed outcome is generated as a Bernoulli variable from .Then, we develop a model to predict based on to , which means that we are using the correct model formulation.Finally, we validate on a new dataset that is generated with the exact same procedure.We repeat this process four times to illustrate the influence of random variability: (1) using 100 simulated patients for development and 100 for validation, (2) using 100 for development and 10,000 for validation, (3) using 10,000 for development and 100 for validation, (4) using 10,000 for development and 10,000 for validation, and (5) using 10 million for development and 10 million for validation.Estimation of model coefficients was unstable when the development data contains only 100 patients (Table 1), despite having more than 10 events per variable (4 parameters, 44 non-events, 56 events).Risk estimates were overfitted, as evidenced by the calibration slope and calibration curves at validation (Example 2 in Table 1, and Figure 1B) as well as the calibration curves.Further, the calibration intercept was negative which is suggestive of general overestimation.Enlarging the development dataset alleviated the problems (Example 4 in Table 1, Figure 1D).Similar issues emerge when using a small validation dataset (Figures 1A and 1C.Calibration results for Examples 1 and 3 are unstable, in particular the flexible calibration curve.With 10 million patients the coefficient estimates and calibration results were perfect (Table 1, Figure 1E).
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results suggest that the flexible calibration curve is more variable and hence requires more data than the calibration slope for a stable assessment.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 8. Sample size at external validation
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is clear that observed calibration curves will easily deviate from the diagonal line even when the model is moderately or strongly calibrated.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 8. Sample size at external validation
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Nevertheless, the decision curve for the true model is the best one.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 4. Calibration, decision-making, and clinical utility
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Again, only the flexible calibration curve reveals that calibration is problematic.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 3.4. Missing non-linearity or interaction: examples 6-7
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The moderately recalibrated version does not have that problem anymore: Net Benefit is now at least as high as that of the default strategies.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 4. Calibration, decision-making, and clinical utility
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The original model has Net Benefit below that of treat all or treat none for a subset of risk thresholds (Figure 5).”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 4. Calibration, decision-making, and clinical utility
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For larger development datasets the disagreement between observed and predicted risks disappeared (Examples 3-5).”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 3.1. Level 1: mean calibration (Calibration-in-the-large)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“results indicate that a flexible assessment of calibration in small validation datasets is problematic.”
- Also stated in: § 2.2. Illustration: Examples 1-5 For illustration, we consider five simulated examples, as previously presented [5].We randomly generate four independent predictor variables to .These predictor variables are ordinal with three categories (-1, 0, and 1) that each have 33% prevalence in order to visualize calibration by covariate pattern.Let outcome be generated by an underlying logistic regression model with the true linear predictor , hence the intercept equals 0, and to are assumed to have linear main effects [5].In simulations, the true probability of event is calculated using the linear predictor and the observed outcome is generated as a Bernoulli variable from .Then, we develop a model to predict based on to , which means that we are using the correct model formulation.Finally, we validate on a new dataset that is generated with the exact same procedure.We repeat this process four times to illustrate the influence of random variability: (1) using 100 simulated patients for development and 100 for validation, (2) using 100 for development and 10,000 for validation, (3) using 10,000 for development and 100 for validation, (4) using 10,000 for development and 10,000 for validation, and (5) using 10 million for development and 10 million for validation.Estimation of model coefficients was unstable when the development data contains only 100 patients (Table 1), despite having more than 10 events per variable (4 parameters, 44 non-events, 56 events).Risk estimates were overfitted, as evidenced by the calibration slope and calibration curves at validation (Example 2 in Table 1, and Figure 1B) as well as the calibration curves.Further, the calibration intercept was negative which is suggestive of general overestimation.Enlarging the development dataset alleviated the problems (Example 4 in Table 1, Figure 1D).Similar issues emerge when using a small validation dataset (Figures 1A and 1C.Calibration results for Examples 1 and 3 are unstable, in particular the flexible calibration curve.With 10 million patients the coefficient estimates and calibration results were perfect (Table 1, Figure 1E). · plain
“Calibration results for Examples 1 and 3 are unstable, in particular the flexible calibration curve.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § 2.2. Illustration: Examples 1-5 For illustration, we consider five simulated examples, as previously presented [5].We randomly generate four independent predictor variables to .These predictor variables are ordinal with three categories (-1, 0, and 1) that each have 33% prevalence in order to visualize calibration by covariate pattern.Let outcome be generated by an underlying logistic regression model with the true linear predictor , hence the intercept equals 0, and to are assumed to have linear main effects [5].In simulations, the true probability of event is calculated using the linear predictor and the observed outcome is generated as a Bernoulli variable from .Then, we develop a model to predict based on to , which means that we are using the correct model formulation.Finally, we validate on a new dataset that is generated with the exact same procedure.We repeat this process four times to illustrate the influence of random variability: (1) using 100 simulated patients for development and 100 for validation, (2) using 100 for development and 10,000 for validation, (3) using 10,000 for development and 100 for validation, (4) using 10,000 for development and 10,000 for validation, and (5) using 10 million for development and 10 million for validation.Estimation of model coefficients was unstable when the development data contains only 100 patients (Table 1), despite having more than 10 events per variable (4 parameters, 44 non-events, 56 events).Risk estimates were overfitted, as evidenced by the calibration slope and calibration curves at validation (Example 2 in Table 1, and Figure 1B) as well as the calibration curves.Further, the calibration intercept was negative which is suggestive of general overestimation.Enlarging the development dataset alleviated the problems (Example 4 in Table 1, Figure 1D).Similar issues emerge when using a small validation dataset (Figures 1A and 1C.Calibration results for Examples 1 and 3 are unstable, in particular the flexible calibration curve.With 10 million patients the coefficient estimates and calibration results were perfect (Table 1, Figure 1E). · plain
- It may state two separate claims as one. 14
- Probably not a substantial claim. 9
- The quote only partly states this claim. 1
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This model exhibits perfect strong calibration: both calibration curves and all covariate patterns coincide with the diagonal.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 3.6. Model misspecification: Examples 8-9
Strength:plain· not yet calibratedn = 10Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Thus, the model is weakly but not moderately calibrated on the development data.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 3.4. Missing non-linearity or interaction: examples 6-7
Strength:plain· not yet calibratedn = 10,000Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This model exhibits perfect moderate calibration but lacks strong calibration because results per covariate pattern are scattered around the diagonal.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 3.6. Model misspecification: Examples 8-9
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With 10 million patients the coefficient estimates and calibration results were perfect (Table 1, Figure 1E).”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 2.2. Illustration: Examples 1-5 For illustration, we consider five simulated examples, as previously presented [5].We randomly generate four independent predictor variables to .These predictor variables are ordinal with three categories (-1, 0, and 1) that each have 33% prevalence in order to visualize calibration by covariate pattern.Let outcome be generated by an underlying logistic regression model with the true linear predictor , hence the intercept equals 0, and to are assumed to have linear main effects [5].In simulations, the true probability of event is calculated using the linear predictor and the observed outcome is generated as a Bernoulli variable from .Then, we develop a model to predict based on to , which means that we are using the correct model formulation.Finally, we validate on a new dataset that is generated with the exact same procedure.We repeat this process four times to illustrate the influence of random variability: (1) using 100 simulated patients for development and 100 for validation, (2) using 100 for development and 10,000 for validation, (3) using 10,000 for development and 100 for validation, (4) using 10,000 for development and 10,000 for validation, and (5) using 10 million for development and 10 million for validation.Estimation of model coefficients was unstable when the development data contains only 100 patients (Table 1), despite having more than 10 events per variable (4 parameters, 44 non-events, 56 events).Risk estimates were overfitted, as evidenced by the calibration slope and calibration curves at validation (Example 2 in Table 1, and Figure 1B) as well as the calibration curves.Further, the calibration intercept was negative which is suggestive of general overestimation.Enlarging the development dataset alleviated the problems (Example 4 in Table 1, Figure 1D).Similar issues emerge when using a small validation dataset (Figures 1A and 1C.Calibration results for Examples 1 and 3 are unstable, in particular the flexible calibration curve.With 10 million patients the coefficient estimates and calibration results were perfect (Table 1, Figure 1E).
Strength:plain· not yet calibratedn = 10Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, strong calibration requires that the model form (e.g. a generalized linear model such as logistic regression) is correct.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 5. Strong calibration: realistic or utopic?
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Refitting should lead to moderate calibration, although this may also require reassessment of nonlinear effects.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 7. A link with model updating
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“in reality, the true model is unknown, may not have a generalized linear form, or may not even exist [22].”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 5. Strong calibration: realistic or utopic?
What the sources say
- Not checked yetcites [22] Chatfield, 1995, Model uncertainty, data mining and statistical inference, J R Stat Soc Ser A source
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes? Checkable: unclear✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In sum, aiming for strong calibration requires the assumption that the model formulation is fully correct, and that unbiased model coefficients as well as unbiased linear predictors are obtained.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 5. Strong calibration: realistic or utopic?
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This method addresses lack of weak calibration, but does not guarantee moderate calibration unless all coefficients were biased by the same rate.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 7. A link with model updating
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we prove (see Appendix) that clinical utility in terms of Net Benefit will not be lower than the default strategies that either classify everyone as having the event or as not having the event.”
- Also stated in: § 6. Moderate calibration: a pragmatic guarantee for non-harmful decision-making · plain
“moderate calibration guarantees that decision-making based on the model is not clinically harmful.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 4. Calibration, decision-making, and clinical utility
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
- Also stated in: § 6. Moderate calibration: a pragmatic guarantee for non-harmful decision-making · plain
In the paper
“For example, strong interactions or nonlinearities may lead to miscalibration in the development sample although weak calibration is perfect [11].”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 3.3. Level 3: moderate calibration
What the sources say
- Not checked yetcites [11] Austin, 2014, Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers, Stat Med source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Third, strong calibration not only requires correctly estimated regression coefficients for the main effects of model predictors, but also requires fully correct modeling of all nonlinear and non-additive effects.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 5. Strong calibration: realistic or utopic?
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For a moderately calibrated model the following property holds, among patients with an estimated risk of % on average out of 100 of these patients have the event.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > Additional information
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
In the paper
“The odds of the risk threshold is the harm-to-benefit ratio, i.e. the ratio of the harm of a false positive and the benefit of a true positive classification[21].”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > cex.d01
What the sources say
- Not checked yetcites [21] Pauker, 1975, Therapeutic decision making: a cost-benefit analysis, N Engl J Med source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“This suggests that moderate calibration prevents the model from becoming harmful [18] but that strong calibration may further enhance the model's clinical utility.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 4. Calibration, decision-making, and clinical utility
What the sources say
- Not checked yetcites [18] Van Calster, 2015, Calibration of risk prediction models: impact on decision-analytic performance, Med Decis Making source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This will only address calibration-in-the-large, but does not guarantee weak calibration.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 7. A link with model updating
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“While still providing sensible risk predictions, simple models that are for example moderately but not strongly calibrated often have many practical advantages such as transparency or ease of use [33].”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 6. Moderate calibration: a pragmatic guarantee for non-harmful decision-making
What the sources say
- Not checked yetcites [33] Marewski, 2012, Heuristic decision making in medicine, Dialogues Clin Neurosci source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This means that at least part of the measurement error is systematic, and further thwarts the concept of the existence of a true model.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 5. Strong calibration: realistic or utopic?
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Calibration is a pivotal aspect of model performance [2][3][4]”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 1. Introduction
What the sources say
- Not checked yetcites [2] Steyerberg, 2009
- Not checked yetcites [3] Kim, 2011, Probabilistic classifiers with high-dimensional data, Biostatistics source
- Not checked yetcites [4] Pepe, 2013, Methods for evaluating prediction performance of biomarkers and tests
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
In the paper
“measurement error of the predictors is in practice often ignored although it is known that this is a common phenomenon that can bias regression coefficients [28].”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 5. Strong calibration: realistic or utopic?
What the sources say
- Not checked yetcites [28] Fuller, 1987
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the model's Net Benefit is lower than that of a default strategy, then using the model to support decision-making can be considered as clinically harmful.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > cex.d01
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“In previous simulations, the Hosmer-Lemeshow test showed such poor performance that it may not be recommended for routine use [7,37].”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > 9. Statistical testing for calibration
What the sources say
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Net Benefit accounts for the different consequences of true and false positives through the risk threshold, i.e. the risk at which one is indifferent about classifying a patient as having the event (and providing treatment) or not.”
§ A calibration hierarchy for risk models was defined: from utopia to empirical data > cex.d01
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Calibrated risk models are vital for valid decision support.
“Calibrated risk models are vital for valid decision support.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“These approaches can be classified into three increasingly stringent levels, labelled as mean, weak, and moderate calibration. 52”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [52]
In the citing paper
“All discussed calibration measures are semi-proper, 52”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [52]
In the citing paper
“Estimated probabilities with too much spread are on average too close to 0 and 1 (slope < 1), whereas estimated probabilities with too little spread are on average too close to the prevalence (slope > 1).”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [52]
In the citing paper
“Previous recommendations were to include at least 100 to 200 individuals in the smallest outcome category.”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [52]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Figure 5 identifies the wrong example
MinorThe discussion says Figure 5 compares decision curves for Example 9, the miscalibrated model. The figure caption instead identifies Example 8, which the paper describes as moderately calibrated.
“Let us consider Example 9 (Figure 3C).The model presented is not calibrated in a weak sense.We derived decision curves to assess the clinical utility of (1) the original miscalibrated model, (2) a recalibrated model to ensure moderate calibration, and (3) the strongly calibrated true model (Figure 5).”
“Figure: 5 Figure 5 . Figure 5. Decision curves for Example 8 to assess clinical usefulness.”
Citations
Cites
Regression Modeling Strategies
Harrell, F. E. (2001) · Springer series in statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,270
Decision curve analysis: a novel method for evaluating prediction models
Vickers, A. J., & Elkin, E. B. (2006) · Medical Decision Making · doi:10.1177/0272989x06295361 · Cited by 6,341
Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures
Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M. J., & Kattan, M. W. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181c30fb2 · Cited by 5,170
Self-citationClinical Prediction Models: A Practical Approach to Development, Validation, and Updating
Steyerberg, E. W. (2019) · Statistics for Biology and Health · doi:10.1007/978-3-030-16399-0 · Cited by 3,281
Self-citationStefanski, L. A., Fuller, W. A., & Wiley, J. (1988) · Journal of Business and Economic Statistics · doi:10.2307/1391893 · Cited by 2,825
Dichotomizing continuous predictors in multiple regression: a bad idea
Royston, P., Altman, D. G., & Sauerbrei, W. (2006) · Statistics in Medicine · doi:10.1002/sim.2331 · Cited by 2,312
Rao, S. J. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s263 · Cited by 1,967
The number of subjects per variable required in linear regression analyses
Austin, P. C., & Steyerberg, E. W. (2015) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2014.12.014 · Cited by 1,145
Self-citationModel uncertainty, data mining and statistical inference
Chatfield, C. (1995) · Journal of the Royal Statistical Society. Series A (Statistics in Society) · doi:10.2307/2983440 · Cited by 1,103
Clinical Prediction Models: A Practical Approach to Development, Validation and Updating
Mann, C. J. H. (2009) · Kybernetes · doi:10.1108/k.2009.06738fae.002 · Cited by 794
Flagged first, then by citations
Cited by
Sorted by citations
Items 401–500 of 918
Kwarah, W., Vroom, F. B. D.-C., Dwomoh, D., & Bosomprah, S. (2025) · BMC Global and Public Health · doi:10.1186/s44263-025-00184-4 · Cited by 7
Xie, X.-R., Yu, M., Xu, R. H., Liu, Y., & Zhang, J. (2025) · Risk Management and Healthcare Policy · doi:10.2147/rmhp.s542262 · Cited by 7
Trottini, M., Campus, G., Corridore, D., Cocco, F., Cagetti, M. G., Vigo, I., Polimeni, A., & Bossù, M. (2020) · Caries Research · doi:10.1159/000507276 · Cited by 6
Xu, Z., Li, J., Cao, Z.-Y., Xu, H., Ying, Y., Xu, Z.-H., Liu, R.-J., Guo, Y., Zhang, Z., Wang, W., et al. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000001293 · Cited by 6
Improving Inverse Probability Weighting by Post-calibrating Its Propensity Scores
Gutman, R., Karavani, E., & Shimoni, Y. (2024) · Epidemiology · doi:10.1097/ede.0000000000001733 · Cited by 6
Durmaz, E. Ş., Karabacak, M., Ozkara, B. B., Kargın, O. A., Demir, B., Raimoglou, D., Aygun, A. A., Adaletli, İ., Baş, A., & Durmaz, E. (2024) · Acta Radiologica · doi:10.1177/02841851241283041 · Cited by 7
Tano, S., Kotani, T., Ushida, T., Matsuo, S., Yoshihara, M., Imai, K., Kinoshita, F., Moriyama, Y., Nomoto, M., Yoshida, S., et al. (2024) · Hypertension Research · doi:10.1038/s41440-024-02024-8 · Cited by 6
Alemu, M., Alemu, S. M., Bagheri, N., Wangdi, K., & Chateau, D. G. (2025) · Open Heart · doi:10.1136/openhrt-2024-003147 · Cited by 6
Cardiac risk prediction model for pregnant women with structural heart disease in Eastern China
Fu, Q., Wang, X., Wang, B., & Lin, J. (2019) · International Journal of Gynecology & Obstetrics · doi:10.1002/ijgo.12820 · Cited by 6
Wang, Z. S., Yap, J. J. L., Koh, Y. L. E., Chia, S. Y., Nivedita, N., Ang, T. W. A., Goh, S. C. P., Lee, C. S., Tan, L. L. J., Ooi, C. W., et al. (2021) · Journal of General Internal Medicine · doi:10.1007/s11606-021-06701-z · Cited by 6
Meid, A. D., González‐González, A. I., Dinh, T. S., Blom, J. W., van den Akker, M., Elders, P. J. M., Thiem, U., de Gaudry, D. K., Swart, K. M. A., Rudolf, H., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-045572 · Cited by 6
A Comparison of Methods to Detect Changes in Prediction Models
Schnellinger, E. M., Yang, W. P., Harhay, M. O., & Kimmel, S. E. (2022) · Methods of Information in Medicine · doi:10.1055/s-0042-1742672 · Cited by 6
Whole Blood Metabolite Profiles Reflect Changes in Energy Metabolism in Heart Failure
Beuchel, C. F., Dittrich, J., Pott, J., Henger, S., Beutner, F., Isermann, B., Loeffler, M., Thiery, J., Ceglarek, U., & Scholz, M. R. (2022) · Metabolites · doi:10.3390/metabo12030216 · Cited by 5
External Validation of a Multivariable Prediction Model for Placenta Accreta Spectrum
Singh, S., Carusi, D. A., Wang, P., Reitman-Ivashkov, E., Landau, R., Fields, K. G., Weiniger, C. F., & Farber, M. K. (2022) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000006222 · Cited by 6
External validation of a prediction model for timely implementation of innovations in radiotherapy
Swart, R., Fijten, R., Boersma, L. J., Kalendralis, P., Behrendt, M., Ketelaars, M., Roumen, C., & Jacobs, M. (2023) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2022.109459 · Cited by 6
Emani, S. S., Swaminathan, A., Grobman, B., Duvall, J. B., López, I., Arnaout, O. M., & Huang, K. T. (2023) · Neurosurgical FOCUS · doi:10.3171/2023.3.focus2352 · Cited by 7
Teressa, G. G., Zhang, M., Lavenburg, P., Cantor, G., Noack, P. S., Yang, J., Goyfman, M., Feldmann, E. J., Butler, J., & Poon, M. (2018) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2018.07.005 · Cited by 6
Demler, O., Paynter, N. P., & Cook, N. R. (2018) · Diagnostic and Prognostic Research · doi:10.1186/s41512-018-0034-5 · Cited by 6
A cardiovascular risk score for hypertensive patients previously admitted to hospital
Martínez‐Díaz, A. M., Palazón‐Bru, A., la Rosa, D. M. F.-D., Ramírez-Prado, D., Llópez‐Espinós, P., Beneyto‐Ripoll, C., & Gil-Guillén, V. F. (2019) · European Journal of Cardiovascular Nursing · doi:10.1177/1474515119845791 · Cited by 6
Daines, L., Bonnett, L. J., Boyd, A., Turner, S., Lewis, S. C., Sheikh, A., & Pinnock, H. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15751.1 · Cited by 6
Wang, K., Deng, Y., Stewart, D. E., & Formica, R. N. (2022) · Journal of the American Society of Nephrology · doi:10.1681/asn.2022010078 · Cited by 6
Ni, H., Groenwold, R. H. H., Nielen, M., & Klugkist, I. G. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0543-5 · Cited by 6
Predicting prolonged sick leave among trauma survivors
von Oelreich, E., Eriksson, M., Brattström, O., Discacciati, A., Strömmer, L., Oldner, A., & Larsson, E. (2019) · Scientific Reports · doi:10.1038/s41598-018-37289-w · Cited by 6
Prasad, A., Brehm, C. E., Goldenberg, M. D. F., Ghodsizad, A., Koerner, M. M., El‐Banayosy, A., & Singbartl, K. (2019) · Artificial Organs · doi:10.1111/aor.13590 · Cited by 6
Taylor, M., Martin, G. P., Abah, U. D., Sperrin, M., Smith, M. O., Bhullar, D., Shackcloth, M. J., Woolley, S., West, D., Shah, R. D., et al. (2021) · Interactive Cardiovascular and Thoracic Surgery · doi:10.1093/icvts/ivab200 · Cited by 6
Takegami, N., Espin, A. T., Imagawa, Y., Watanabe, I., Rowell, S., Schreiber, M. A., Ferguson, A. R., & Hinson, H. E. (2024) · Journal of Neurotrauma · doi:10.1089/neu.2024.0158 · Cited by 5
Lin, A. L., Parrish, A. B., Cary, M. P., Silcox, C., Balu, S., Jelovsek, J. E., O’Brien, C. L., Pencina, M. J., Poon, E. G., & Economou-Zavlanos, N. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-094601 · Cited by 7
Self-citationAlves, K. S., Shah, D. A., Dillard, H. R., Ponte, E. M. D., & Pethybridge, S. J. (2025) · Phytopathology · doi:10.1094/phyto-01-25-0008-fi · Cited by 5
Yu, B., Cho, J., Kim, H., Hwang, S. H., Hwang, J., Kim, S., Oh, J. S., Lee, S., Kim, D. W., Seok, J., et al. (2025) · BJS Open · doi:10.1093/bjsopen/zraf167 · Cited by 5
Zhang, D., & Zhao, Y. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1156230 · Cited by 5
Garbern, S. C., Islam, M. T., Islam, K., Ahmed, S. M., Brintz, B. J., Khan, A. I., Taniuchi, M., Platts-Mills, J. A., Qadri, F., & Leung, D. T. (2023) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofad295 · Cited by 5
Liu, Y.-G., Jiang, S., Zhang, J., Zhang, L., Zhao, H., Sang, X., Lü, X., & Xu, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-52025-3 · Cited by 5
Bräuner, K. B., Tsouchnika, A., Mashkoor, M., Williams, R. D., Rosen, A. W., Hartwig, M. F. S., Bulut, M., Dohrn, N., Rijnbeek, P. R., & Gögenür, I. (2024) · International Journal of Colorectal Disease · doi:10.1007/s00384-024-04607-w · Cited by 5
Lapi, F., Marconi, E., Lombardo, F. P., Cricelli, I., Ansaldo, E., Gorini, M., Micheletto, C., Marco, F. D., & Cricelli, C. A. (2024) · Respiratory Medicine · doi:10.1016/j.rmed.2024.107634 · Cited by 5
Non‐parametric inference on calibration of predicted risks
Sadatsafavi, M., & Petkau, J. (2024) · Statistics in Medicine · doi:10.1002/sim.10138 · Cited by 5
Luo, H., Liu, X., Yang, Y., Tang, B., He, P., Ding, L., Wang, Z., & Shi, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-06936-4 · Cited by 5
Out-of-Sample Validity of the PROLOGUE Score to Predict Neurologic Function after Cardiac Arrest
Schriefl, C., Schoergenhofer, C., Buchtele, N., Müeller, M., Poppe, M., Clodi, C., Ettl, F., Merrelaar, A. E., Boegl, M. S., Steininger, P., et al. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12060876 · Cited by 5
Ghosheh, G. O., Alamad, B., Yang, K.-W., Syed, F., Hayat, N., Iqbal, I., Kindi, F. A., Junaibi, S. A., Safi, M. A., Ali, R., et al. (2022) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2022.100065 · Cited by 7
Xie, B., Chen, Z., Luo, J., Lei, Y., Li, J. J., Wu, R., Wang, Q., & Liu, X. (2022) · Disease Markers · doi:10.1155/2022/5427579 · Cited by 5
Guiding Risk Adjustment Models Toward Machine Learning Methods
Weissman, G. E., & Maddox, K. E. J. (2023) · JAMA · doi:10.1001/jama.2023.12920 · Cited by 5
Spiero, I., Schuit, E., Wijers, O. B., Hoebers, F. J. P., Langendijk, J. A., & Leeuwenberg, A. M. (2023) · Clinical and Translational Radiation Oncology · doi:10.1016/j.ctro.2023.100677 · Cited by 5
van Beek, S., Nieboer, D., Klimek, M., Stolker, R. J., & Mijderwijk, H.-J. (2024) · Scientific Reports · doi:10.1038/s41598-023-50518-1 · Cited by 4
Self-citationCovidogram as a simple tool for predicting severe course of COVID-19: population-based study
Jarkovský, J., Benešová, K., Černý, V., Rážová, J., Kala, P., Dolina, J., Májek, O., Šebestová, S., Bezděková, M., Melicharová, H., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-045442 · Cited by 5
Zheng, S., Lyu, J., Han, D., Xu, F., Li, C., Yang, R., Lu, Y., Wu, Y., & Tian, G. (2021) · Journal of International Medical Research · doi:10.1177/03000605211011976 · Cited by 5
Predicting Prolonged Apnea During Nurse-Administered Procedural Sedation: Machine Learning Study
Conway, A., Jungquist, C. R., Chang, K., Kamboj, N., Sutherland, J., Mafeld, S. C., & Parotto, M. (2021) · JMIR Perioperative Medicine · doi:10.2196/29200 · Cited by 5
Prognostic model for predicting survival in very preterm infants: an external validation study
van Beek, P. E., Groenendaal, F., Onland, W., Koole, S., Dijk, P. H., Dijkman, K. P., van den Dungen, F., van Heijst, A., Kornelisse, R. F., Schuerman, F., et al. (2021) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.17010 · Cited by 5
König, S., Pellissier, V., Leiner, J., Hohenstein, S., Ueberham, L., Meier‐Hellmann, A., Kuhlen, R., Hindricks, G., & Bollmann, A. (2021) · Clinical Cardiology · doi:10.1002/clc.23762 · Cited by 5
Prediction of outcomes after second-line treatment for acute graft-versus-host disease
Vo, P., Gooley, T. A., Carpenter, P. A., Sorror, M. L., MacMillan, M. L., DeFor, T. E., & Martin, P. J. (2022) · Blood Advances · doi:10.1182/bloodadvances.2021006220 · Cited by 5
Gérardin, P., Maillard, O., Bruneau, L., Accot, F., Legrand, F., Poubeau, P., MANAQUIN, R., Andry, F., Bertolotti, A., & Levin, C. (2021) · Travel Medicine and Infectious Disease · doi:10.1016/j.tmaid.2021.102232 · Cited by 5
Systematic Reviews of Prediction Models
Collins, P. G. S., Moons, K. G. M., Debray, T. P. A., Altman, D. G., & Riley, R. D. (2022) · Wiley eBooks · doi:10.1002/9781119099369.ch18 · Cited by 6
Bernardini, B., Baratto, L., Pizzi, C., Biggeri, A., Cerina, G., Colantonio, V., Corsini, C., Ghirmai, S., Pagani, M., Fracchia, S., et al. (2022) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2022.10.021 · Cited by 5
Lee, C., Lui, D. T. W., Cheung, C. Y. Y., Fong, C. H.-Y., Yuen, M. M. A., Chow, W.-S., Xu, A., & Lam, K. S. L. (2023) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfad034 · Cited by 5
Hlaing, W. M., & Zhu, Y. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99967-0.00224-6 · Cited by 6
Shang, H., Shi, X., Yu, H., Feng, Y., Huang, Y., Guo, N., Guo, X., Guo, Q., Wang, X., & Sun, J. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1786335 · Cited by 4
Kokkinakis, S., Kritsotakis, E. I., Paterakis, K. N., Karali, G., Malikides, V., Kyprianou, A., Papalexandraki, M., Anastasiadis, C. S., Zoras, O., Drakos, N., et al. (2023) · European Journal of Trauma and Emergency Surgery · doi:10.1007/s00068-023-02351-4 · Cited by 4
Yan, W., Yao, Z., Ou, Q., & Ye, G. (2023) · BMC Gastroenterology · doi:10.1186/s12876-023-02967-1 · Cited by 4
Diagnostic and prognostic performance of the LiverRisk score in tertiary care
Semmler, G., Balcar, L., Simbrunner, B., Hartl, L., Jachs, M., Schwarz, M., Hofer, B., Fritz, L., Schedlbauer, A., Stopfer, K., et al. (2024) · JHEP Reports · doi:10.1016/j.jhepr.2024.101169 · Cited by 4
Fracture risk assessment in the presence of competing risk of death
Tran, T. S., Bliuc, D., Blank, R. D., & Nguyen, T. V. (2024) · Osteoporosis International · doi:10.1007/s00198-024-07224-z · Cited by 4
Villar, J., González-Martín, J. M., Fernández, C., Añón, J. M., Ferrando, C., Mora-Ordóñez, J. M., Martínez, D., Mosteiro, F., Ambrós, A., Fernández, L., et al. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14051711 · Cited by 4
Self-citationMacCarthy, G., & Pazoki, R. (2025) · Healthcare · doi:10.3390/healthcare13091003 · Cited by 5
Ghadban, Y. A., Astbury, N. M., Kurdi, A., Sharma, A., Ope, B. W., Liu, T., Mackillop, L. H., Lu, H. Y., & Hirst, J. E. (2025) · Obesity Reviews · doi:10.1111/obr.13934 · Cited by 4
Liu, Y., Tan, Z. D., Zhang, Z., Wang, S., Guo, J. S., Liu, H., Chen, T., & Bian, J. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114102 · Cited by 5
Fu, Y., Jenks, A. D., Rubinstein, S. M., de Luca, K. E., Axén, I., Koes, B. W., & Chiarotto, A. (2025) · Chiropractic & Manual Therapies · doi:10.1186/s12998-025-00592-1 · Cited by 4
Clustered flexible calibration plots for binary outcomes using random effects modeling
Barreñada, L., Cock, B. D., Wynants, L., & CALSTER, B. V. (2025) · Research Synthesis Methods · doi:10.1017/rsm.2025.10046 · Cited by 5
Moro, F., Ciancia, M., Berardino, S. D., Baldassari, G., Mascilini, F., Ciccarone, F., Nero, C., Tran, H. E., Pasciuto, T., Boldrini, L., et al. (2026) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.70203 · Cited by 4
Bicknell, R., Lim, W. K., Maier, A. B., & LoGiudice, D. (2020) · Diagnostic and Prognostic Research · doi:10.1186/s41512-020-00085-0 · Cited by 4
Boon, H. A., Verbakel, J. Y. J., Burghgraeve, T. D., & den Bruel, A. V. (2022) · BJGP Open · doi:10.3399/bjgpo.2021.0171 · Cited by 4
Reilly, J. R., Wong, D. J., Brown, W. A., Gabbe, B. J., & Myles, P. S. (2022) · ANZ Journal of Surgery · doi:10.1111/ans.17946 · Cited by 4
Ahmed, A., Ambler, G., Pereira, S. M. P., LENNON, L. T., Papacosta, O., Whincup, P. H., & Wannamethee, S. G. (2022) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2022.102098 · Cited by 4
Pate, A., Sperrin, M., Riley, R. D., Sergeant, J. C., van Staa, T. P., Peek, N. B., Mamas, M. A., Lip, G. Y. H., O’Flaherty, M., Buchan, I. E., et al. (2023) · Statistics in Medicine · doi:10.1002/sim.9771 · Cited by 4
Marko, M., Goyal, M., Ospel, J. M., Singh, N., Venema, E., Nogueira, R. G., Demchuk, A. M., McTaggart, R., Poppe, A. Y., Menon, B. K., et al. (2023) · Interventional Neuroradiology · doi:10.1177/15910199231221491 · Cited by 4
Prediction Models for Adverse Drug Reactions During Tuberculosis Treatment in Brazil
Ridolfi, F. M., Amorim, G., Peetluk, L. S., Haas, D. W., Staats, C., Araújo‐Pereira, M., Cordeiro‐Santos, M., Kritski, A. L., Figueiredo, M. C., Andrade, B. B., et al. (2024) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiae025 · Cited by 4
Automatic Voice Disorder Detection from a Practical Perspective
Vidal, J., Ribas, D., Bonomi, C., Lleida, E., Ferrer, L., & Ortega, A. (2024) · Journal of Voice · doi:10.1016/j.jvoice.2024.03.001 · Cited by 4
Liu, C., Xiong, Y., Zhao, P., Chen, M., Wei, W., Sun, X., Liu, X., & Tan, J. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111424 · Cited by 4
Zhang, Y., Weijer, R. H. A., van Schooten, K. S., Bruijn, S. M., & Pijnappels, M. A. G. M. (2024) · Journal of the American Medical Directors Association · doi:10.1016/j.jamda.2024.105107 · Cited by 4
von Ostau, N. E., Handke, A. E., Wiesenfarth, M., Albers, P., Antoch, G., Noldus, J., Reis, H., Cotarelo, C., Preetz, J., Umutlu, L., et al. (2024) · World Journal of Urology · doi:10.1007/s00345-024-05232-6 · Cited by 4
Casacchia, N. J., Lenoir, K. M., Rigdon, J., & Wells, B. J. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02803-w · Cited by 4
Li, S. R., Mazroua, M. S., Reitz, K. M., Phillips, A. R., Tzeng, E., & Liang, N. L. (2025) · European Journal of Vascular and Endovascular Surgery · doi:10.1016/j.ejvs.2025.02.017 · Cited by 4
Bian, Z., Dou, W., Ying, Y., Shi, H., Ji, F., Hu, J.-H., & Peng, K. (2025) · BMC Anesthesiology · doi:10.1186/s12871-025-03090-2 · Cited by 4
Zhou, L., Liang, S., Qin, S., Fan, C., Gao, L., & Cai, W. (2022) · PeerJ · doi:10.7717/peerj.13388 · Cited by 4
Faisal, M., Mohammed, M., Richardson, D., Fiori, M., & Beatson, K. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-050274 · Cited by 4
Baysan, M., Arbous, M. S., Steyerberg, E. W., & van der Bom, J. G. (2022) · Critical Care Explorations · doi:10.1097/cce.0000000000000750 · Cited by 5
Self-citationDevelopment of artificial neural network models for paediatric critical illness in South Africa
Pienaar, M. A., Sempa, J. B., Luwes, N. J., George, E. C., & Brown, S. C. (2022) · Frontiers in Pediatrics · doi:10.3389/fped.2022.1008840 · Cited by 4
Takeuchi, M., Matsunaga, M., Egashira, R., Miyake, A., Yasuno, F., Nakano, M., Moriguchi, M., Tonari, S., Hotta, S., Hayashi, H., et al. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.969833 · Cited by 4
Bos, D. A. G., Burghgraeve, T. D., Sutter, A. D., Buntinx, F., & Verbakel, J. Y. J. (2023) · BMC Medicine · doi:10.1186/s12916-023-02860-4 · Cited by 4
Martin, A. J., van der Velden, F. J. S., von Both, U., Tsolia, M. N., Zenz, W., Sagmeister, M. G., Vermont, C., de Vries, G., Kolberg, L., Lim, E. J., et al. (2023) · Archives of Disease in Childhood · doi:10.1136/archdischild-2023-325869 · Cited by 4
Development and Validation of a Nomogram for Predicting Obstructive Sleep Apnea Severity in Children
Liu, Y., Xie, S., Yang, X., Chen, J. L., & Zhou, J. R. (2024) · Nature and Science of Sleep · doi:10.2147/nss.s445469 · Cited by 4
Model Recalibration for Regional Bias Reduction in Dynamic Microsimulations
Weymeirsch, J., Ernst, J., & Münnich, R. T. (2024) · Mathematics · doi:10.3390/math12101550 · Cited by 4
Deng, G., Bi, B., Deng, H., Fan, J., Huang, Z., Zhang, C., & He, Y. (2024) · World Journal of Surgical Oncology · doi:10.1186/s12957-024-03486-3 · Cited by 4
Ma, Y., Li, J., Tan, X., Cai, M., Zhang, X., & Ma, J. (2022) · International Journal of Women s Health · doi:10.2147/ijwh.s386689 · Cited by 4
ten Haaf, K., & Steyerberg, E. W. (2016) · European Journal of Epidemiology · doi:10.1007/s10654-016-0206-y · Cited by 4
Self-citationApplication of clinical prediction modeling in pediatric neurosurgery: a case study
Mijderwijk, H.-J., Beez, T., Hänggi, D., & Nieboer, D. (2021) · Child s Nervous System · doi:10.1007/s00381-021-05112-z · Cited by 4
Self-citationComparing methods addressing multi-collinearity when developing prediction modelsPreprint
Leeuwenberg, A. M., van Smeden, M., Langendijk, J. A., van der Schaaf, A., Mauer, M. E. L., Moons, K. G. M., Reitsma, J. B., & Schuit, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.01603 · Cited by 4
Comparisons of Risk Scores for Infective Endocarditis Surgery: A Meta-Analysis
Agrawal, A., Arockiam, A. D., Dahdah, J. E., Honnekeri, B., Schleicher, M. C., Shekhar, S., Haroun, E., Witten, J. C., Majid, M., Pettersson, G., et al. (2025) · Angiology · doi:10.1177/00033197241310571 · Cited by 3
Mari, T., Ali, H. H., Pacinotti, L., Powsey, S., & Fallon, N. B. (2025) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-025-01268-2 · Cited by 3
Classification and predictive models using supervised machine learning: A conceptual review
Pienaar, M. A., & Naidoo, K. D. (2025) · Southern African Journal of Critical Care · doi:10.7196/sajcc.2025.v411.2937 · Cited by 5
Heo, K., Jeong, J., Shin, J. H., Shin, J., Lee, H., Kwon, H., & Lee, J. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.125.043979 · Cited by 3
Lv, Y., Luo, Z., Jin, X., Lu, Z., & Han, P. (2025) · European journal of medical research · doi:10.1186/s40001-025-03441-2 · Cited by 3
From Logistic Regression to Foundation Models: Factors Associated With Improved Forecasts
Alabi, A., Akinpeloye, O., Izinyon, O. C., Amusa, T., & Famotire, A. V. (2025) · Cureus · doi:10.7759/cureus.96669 · Cited by 3
Lu, Y., Eilen, H., Pareek, A., Varano, M., Wilbur, R. R., Lavoie-Gagne, O. Z., Krych, A. J., Camp, C. L., Okoroha, K. R., & Forsythe, B. (2026) · Journal of ISAKOS Joint Disorders & Orthopaedic Sports Medicine · doi:10.1016/j.jisako.2026.101088 · Cited by 3
Metadata
- Authors
- Van Calster, B., Nieboer, D., Vergouwe, Y., De Cock, B., Pencina, M. J., & Steyerberg, E. W.
- Affiliations
- Erasmus MC (NL)
- Duke University (US)
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2016-06
- Volume
- 74
- Pages
- 167-176
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W2225109326
- Cited by
- 910
- References
- 42
- Citations by year
- 2026: 192 · 2025: 99 · 2024: 118 · 2023: 93 · 2022: 125 · 2021: 91 · 2020: 58 · 2019: 49 · 2018: 35 · 2017: 30 · 2016: 19
- FWCI
- 44.92
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- Locations
- Journal of Clinical Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Funders
- Fonds Wetenschappelijk Onderzoek (G049312N)
- KU Leuven (C24/15/037)
- Vlaamse regering (G049312N, C24/15/037)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- risk model calibration, external validation, decision support, overfitting, simulation study
- MeSH terms
- Calibration, Computer Simulation, Decision Support Techniques, Humans, Risk, Reproducibility of Results, Models, Statistical, Bias, Risk Assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
