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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.
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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).
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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
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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
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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
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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
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- 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.
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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
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Clark, A. L., Omar, R., Ambler, G., Chen, Q., Cleland, J. G. F., Shote, A., Jones, P. D., Wang, J., de Belder, M., Hardman, S. M. C., et al. (2026) · Heart · doi:10.1136/heartjnl-2025-326731 · Cited by 1
Wu, K.-B., Wang, J., Jiang, W., Cai, X., Huang, L., She, B., Song, M., Lai, Z.-H., & Lyu, G. (2026) · European journal of medical research · doi:10.1186/s40001-026-03919-7 · Cited by 1
Long, Z., & Tong, S. (2026) · Shock · doi:10.1097/shk.0000000000002800 · Cited by 1
Hu, H., Zu, Y., Zhao, L., Zhang, Q., Zhou, G., Xu, P., Zhao, A., Yin, F., Sharma, L., & Chang, D. (2026) · Respiratory Research · doi:10.1186/s12931-026-03581-x · Cited by 1
Cross-Population Validation of the Pediatric CKD Risk-Prediction Tool
Park, P. G., Kim, J. W., Choi, N., Kim, J. H., Lim, S. H., Lee, J. H., Park, M. J., Baek, H. S., Cho, M. H., Lee, I. R., et al. (2026) · Kidney International Reports · doi:10.1016/j.ekir.2026.106373 · Cited by 1
Cheun, H., Ahn, J. H., Park, J.-W., Ko, S. H., Shim, J.-K., Lee, H., Cho, Y. J., Nam, K., Ju, J.-W., Chung, J., et al. (2026) · BMC Anesthesiology · doi:10.1186/s12871-026-03841-9 · Cited by 1
Advances in survival analyses: machine learning methods and model comparison
Gately, R., Sabanayagam, D., Lim, W. H., Zhu, L., Boroumand, F., Bakar, K. S., Teixeira-Pinto, A. M., & Wong, G. (2026) · Kidney International · doi:10.1016/j.kint.2026.02.041 · Cited by 1
Vach, W. (2026) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2026.112308 · Cited by 1
Gu, J., Chen, J., Wang, J., Zhu, Z., Huang, L., Deng, Q., Li, Y., You, L., & Zuo, Y. (2026) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s604550 · Cited by 1
Polpitiya, A. D., Cox, C., Butler, H., Badsha, M. B., Sommerville, L. J., Boniface, J. J., Saade, G. R., & Kearney, P. E. (2026) · Diagnostics · doi:10.3390/diagnostics16101487 · Cited by 1
Identifying Pre-Existing Diabetes at ICU Admission with Machine Learning on Public GOSSIS Data
Zhuhadar, L. (2026) · Diabetology · doi:10.3390/diabetology7050100 · Cited by 1
Kim, K., Kim, J., Kim, M.-G., & Han, H. W. (2026) · Digital Health · doi:10.1177/20552076261456585 · Cited by 2
Créon, A., Magnani, M., Maas, C. C. H. M., van Diepen, M., Caldinelli, A., Russel, W. A., Dekker, F. W., Carrero, J. J., & Fu, E. L. (2026) · Clinical Kidney Journal · doi:10.1093/ckj/sfag187 · Cited by 1
Wei, H., Heo, S., Yang, Y., Li, M., Fu, F., Xia, T., Lee, S. S., Choi, E. S., Sartoris, R., Grégory, J., et al. (2026) · Journal of Hepatology · doi:10.1016/j.jhep.2026.05.027 · Cited by 1
Lin, H.-C., Chen, I.-M., Lin, T.-C., Yen, H., Lin, W., & Groot, O. Q. (2026) · Journal of the Formosan Medical Association · doi:10.1016/j.jfma.2026.06.046 · Cited by 1
Yoo, D., Nguyen, V. K., Maggiore, U., & Jolliet, O. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001486 · Cited by 1
Sirago, G., Solarino, B., Dell'Erba, A., & Ferorelli, D. (2026) · Legal Medicine · doi:10.1016/j.legalmed.2026.102931 · Cited by 2
Ivshin, А. А., Boldina, Y. S., & Malyshev, N. A. (2026) · Obstetrics Gynecology and Reproduction · doi:10.17749/2313-7347/ob.gyn.rep.2026.729 · Cited by 1
Andersen, A. K., Mehdizavareh, H., Khan, A., Becher, T., Britsch, S., Britsch, M., Bøttcher, M., Winther, S., Rohde, P. D., Jensen, M. H., et al. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag127 · Cited by 1
External validation of EEG-based machine learning models for continuous pain prediction
Mari, T., Henderson, J., Ali, S. H., Hewitt, D., Brown, C. A., Stančák, A., & Fallon, N. B. (2026) · Journal of Pain · doi:10.1016/j.jpain.2026.106416 · Cited by 3
Kamulegeya, R., Nabatanzi, R., Semugenze, D., Mugala, F., Takuwa, M., Nasinghe, E., Musinguzi, D., Namiiro, S. M., Katumba, A., Ssengooba, W., et al. (2026) · PLoS ONE · doi:10.1371/journal.pone.0355124 · Cited by 2
Yang, M.-A., & Ha, K.-S. (2026) · Bioengineering · doi:10.3390/bioengineering13090965 · Cited by 1
Evolution of Modern Cardiovascular Quality Metrics
Chinedozi, I., & Wessler, B. S. (2020) · Springer eBooks · doi:10.1007/978-3-030-37145-6_5 · Cited by 1
Jin, L., Zou, Y., Ruan, S., Han, H., Zhang, Y., Chen, Z., Jin, H., & Shi, N. (2021) · Gland Surgery · doi:10.21037/gs-20-597 · Cited by 1
Han, F., Liao, S., Wu, R., Liu, S., Zhao, Y., & Xie, Y. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534147 · Cited by 1
Development and validation of a prediction score to assess the risk of depression in primary care
Lapi, F., Castellini, G., Ricca, V., Cricelli, I., Marconi, E., & Cricelli, C. A. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.03.160 · Cited by 1
Qiao, Y., Jia, Y., Luo, L., Li, B., Xie, F., Wang, H., & Li, S. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1342244 · Cited by 1
Meijs, D. A. M., Wynants, L., van Kuijk, S. M. J., Scheeren, C. I. E., Hana, A., Mehagnoul-Schipper, J., Stessel, B., Laenen, M. V., Cox, E. G. M., Sels, J.-W. E. M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-70333-6 · Cited by 1
Liao, K., van der Veer, S. N., Gomes, F., Finn, C. F., Yorke, J., & Sperrin, M. (2024) · JCO Clinical Cancer Informatics · doi:10.1200/cci.24.00035 · Cited by 1
Patient Outcomes Through Machine Learning: A Review Of Data Management Strategies In Healthcare
Uddin, M. M. (2024) · Innovatech Engineering Journal · doi:10.70937/jnes.v1i01.33 · Cited by 1
Kazemi, F., Ghotbi, E., Gendreau, J. L., & Cohen, A. R. (2025) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2025.111308 · Cited by 1
Zwaag, S. M., de Lange, D. W., Brinkman, S., van den Hengel-Koot, I. S., & Hunault, C. C. (2025) · Clinical Toxicology · doi:10.1080/15563650.2025.2543067 · Cited by 1
Hernández‐Cardenas, C. M., Mendoza‐Copa, G., Hong-Zhu, P., Gómez-García, I. A., & Lugo-Goytia, G. (2019) · Revista de investigaci�n Cl�nica · doi:10.24875/ric.19002957 · Cited by 1
Conca, A., Koch, D., Regez, K., Kutz, A., Baechli, C., Haubitz, S., Schüetz, P., Müeller, B., Spirig, R., & Petry, H. (2020) · JMIR Research Protocols · doi:10.2196/21447 · Cited by 1
Palazón‐Bru, A., Prieto-Castelló, M. J., la Rosa, D. M. F.-D., Macanás-Martínez, A., Mares‐García, E., Carbonell‐Torregrosa, M. D. L. Á., Gil-Guillén, V. F., Cardona-Llorens, A. F. J., & Marhuenda-Amorós, D. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17249518 · Cited by 1
Ding, J., Yao, H., & Chen, Q. (2022) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2022/3784300 · Cited by 1
RetractedCALSTER, B. V., van Smeden, M., & Steyerberg, E. W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.11493 · Cited by 1
Self-citationModel-based ROC (mROC) curve: examining the effect of case-mix and model calibration on the ROC plotPreprint
Sadatsafavi, M., Saha‐Chaudhuri, P., & Petkau, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.00316 · Cited by 1
Kocbek, S., Kocbek, P., Cilar, L., & Štiglic, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.13815 · Cited by 1
Stevens, R., & Poppe, K. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.06.001 · Cited by 1
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. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.01138 · Cited by 1
Moser, A., Reinikainen, M., Jakob, S. M., Selander, T. A., Pettilä, V., Kiiski, O., Varpula, T., Raj, R., & Takala, J. A. (2021) · doi:10.31219/osf.io/rb62v · Cited by 1
Kalendralis, P., Sloep, M., George, N. M., Snel, J., Veugen, J., Hoebers, F. J. P., Wesseling, F. W. R., Unipan, M., Veening, M., Langendijk, H., et al. (2022) · doi:10.31219/osf.io/rt7bs · Cited by 1
Fu, H., Li, F., Wei, F., Qu, W., Wu, D., Liang, B., Zhang, X., & Liu, Q. (2022) · Research Square · doi:10.21203/rs.3.rs-2003197/v1 · Cited by 1
Rhodes, S. (2022) · doi:10.31219/osf.io/4n86q · Cited by 1
pmcalibration: Calibration Curves for Clinical Prediction Models
Rhodes, S. (2023) · doi:10.32614/cran.package.pmcalibration · Cited by 1
Moro, F., Momi, M., Bertoldo, V., Ledger, A., Barreñada, L., Ceusters, J., Sturla, D., Ghezzi, F., Mor, E., Fornari, L., et al. (2024) · medRxiv · doi:10.1101/2024.12.23.24319517 · Cited by 1
Agovi, A. M., Thompson, C. T., Meadows, R. J., Lu, Y., & Ojha, R. P. (2025) · medRxiv · doi:10.1101/2025.01.17.25320732 · Cited by 1
Simulating interferon-γ assay testing patterns in Irish bovine tuberculosis breakdownsPreprint
Casey-Bryars, M., Madden, J. M., Conlan, A. J. K., Horan, M., McGrath, G., Tratalos, J. A., & Gormley, E. P. (2025) · Research Square · doi:10.21203/rs.3.rs-7343174/v1 · Cited by 1
Palazón‐Bru, A., Dólera-Moreno, C., la Rosa, D. M. F.-D., Colomina-Climent, F., & Gil-Guillén, V. F. (2016) · International Journal of Clinical Practice · doi:10.1111/ijcp.12897
Trustworthy or flawed clinical prediction rule?
Granholm, A., Perner, A., Jensen, A. K. G., & Møller, M. H. (2018) · Critical Care · doi:10.1186/s13054-018-1961-9
A scoring system to detect fixed airflow limitation in smokers from simple easy-to-use parameters
Conejos, E. R., Palazón‐Bru, A., la Rosa, D. M. F.-D., Sánchez-Molla, M., Baeza, M. R., Gil-Guillén, V. F., & Cortés‐Castell, E. (2018) · Scientific Reports · doi:10.1038/s41598-018-31198-8
A scoring system to predict recurrence in patients with differentiated thyroid cancer
López‐Bru, D., Palazón‐Bru, A., la Rosa, D. M. F.-D., & Gil-Guillén, V. F. (2018) · Clinical Otolaryngology · doi:10.1111/coa.13227
Relapse prevention strategies for recurrent depression
Klein, N. S. (2019) · University of Groningen research database (University of Groningen / Centre for Information Technology)
Predicting respiratory failure in amyotrophic lateral sclerosis: still a long way to go
Ackrivo, J., Elman, L. B., & Kawut, S. M. (2019) · European Respiratory Journal · doi:10.1183/13993003.01221-2019
Harrison‐Brown, M., Scholes, C. J., Sandhu, K. S., Ebrahimi, M., Bell, C., & Kirwan, G. W. (2020) · medRxiv · doi:10.1101/2020.08.24.20180653
Timing van de PSMA PET/CT-scan bij biochemisch recidief na radicale prostatectomie
Luiting, H. B., van Leeuwen, P. J., Remmers, S., Donswijk, M. L., Busstra, M. B., Bakker, I. L., Brabander, T., Stokkel, M. P. M., van der Poel, H. G., & Roobol, M. J. (2020) · Tijdschrift voor Urologie · doi:10.1007/s13629-020-00303-w
Improving the acute and perioperative hemodynamic assessment
Kaufmann, T. (2020) · doi:10.33612/diss.128650137
External validation of biomarkers of fatty liver in the general population: the Bagnacavallo studyPreprint
Foschi, F. G., Conti, F., Domenicali, M., Giacomoni, P., Borghi, A., Bevilacqua, V., Napoli, L., Berardinelli, D., Altini, M., Cucchetti, A., et al. (2020) · doi:10.32388/cd6lgj
Wessler, B. S., Nelson, J., Park, J. G., McGinnes, H. L., Upshaw, J. N., CALSTER, B. V., Klaveren, D. V., Steyerberg, E. W., & Kent, D. M. (2021) · medRxiv · doi:10.1101/2021.01.21.21250234
Self-citationPrediction Models for Tuberculosis Treatment Outcomes
Peetluk, L. S. (2021)
Eyþórsson, E. S. (2021) · medRxiv · doi:10.17605/osf.io/t2bp8
Boyd, J. M., James, M. T., Zuege, D. J., & Stelfox, H. T. (2021) · Research Square · doi:10.21203/rs.3.rs-841701/v1
D-20-7 確率オートマトンに基づく確率文脈自由文法モデルの推論(D-20.情報論的学習理論と機械学習)
啓, 若. (2013) · 電子情報通信学会総合大会講演論文集
A prediction model for placenta accreta spectrum: A multicentre external validation study.Preprint
Singh, S., Carusi, D. A., Wang, P., Reitman-Ivashkov, E., Landau, R., Fields, K. G., Weiniger, C. F., & Farber, M. K. (2021) · doi:10.22541/au.161781507.70585246/v1
Gielis, W. P. (2022) · Utrecht University Repository (Utrecht University) · doi:10.33540/1153
Riley, R. D., Tierney, J. F., & Stewart, L. A. (2021) · Wiley eBooks · doi:10.1002/9781119333784.refs5
Guessing Game of Patient Outcomes in the Renally Injured Critically Ill: Is There a Perfect Score?
Raju, G. M. (2022) · Indian Journal of Critical Care Medicine · doi:10.5005/jp-journals-10071-24177
Ehrman, R. R. (2022) · Academic Emergency Medicine · doi:10.1111/acem.14488
Wada, Y., & Goel, A. (2022) · European Journal of Cancer · doi:10.1016/j.ejca.2022.06.005
Wang, M., & Li, Y. (2022) · European Journal of Cancer · doi:10.1016/j.ejca.2022.06.004
Risk stratification in high-risk patients undergoing lung resection
Taylor, M., Grant, S. W., & Martin, G. P. (2022) · Shanghai Chest · doi:10.21037/shc-22-27
Artificial intelligence applications in radiotherapy
Kalendralis, P. (2022) · doi:10.26481/dis.20221010pk · Cited by 1
Fu, H., Li, F., Wei, F., Qu, W., Wu, D., Liang, B., Zhang, X., & Liu, Q. (2022) · Research Square · doi:10.21203/rs.3.rs-2003197/v2
Acute and critical care diagnostics in COVID-19
Lieveld, A. W. E. (2023) · doi:10.5463/thesis.179
Pate, A., Riley, R. D., Sperrin, M., CALSTER, B. V., Sergeant, J. C., Peek, N. B., van Staa, T. P., Mamas, M. A., Lip, G. Y. H., O’Flaherty, M., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3121017/v1
Biziaev, T., Aktary, M. L., Wang, Q., Chekouo, T., Bhatti, P., Shack, L., Robson, P. J., & Kopciuk, K. A. (2023) · Cancers · doi:10.3390/cancers15143545 · Cited by 1
Jiang, Z., Guo-hu, Z., Xia, J., & Lv, S. (2023) · Risk Management and Healthcare Policy · doi:10.2147/rmhp.s417553
Liu, Y.-G., Jiang, S., Zhang, J., Zhang, L., Zhao, H., Sang, X., Lü, X., & Xu, Y. (2023) · Research Square · doi:10.21203/rs.3.rs-3231947/v1
Wirth, M., Ruckes, C., Michel, M. S., & Schattenberg, J. M. (2023) · AIDS · doi:10.1097/qad.0000000000003779
Fukumoto, H., Oyake, K., Kurobe, Y., Mizoguchi, H., Sakai, T., & Momose, K. (2023) · Research Square · doi:10.21203/rs.3.rs-3639244/v1
Fu, H., Li, F., Wei, F., Qu, W., Wu, D., Liang, B., Zhang, X., & Liu, Q. (2024) · Research Square · doi:10.21203/rs.3.rs-3831221/v1
Harrison‐Brown, M., Scholes, C. J., Ebrahimi, M., Bell, C., & Kirwan, G. W. (2024) · Clinical Rehabilitation · doi:10.1177/02692155241233348
Predicting absolute risk for a person with missing risk factors
Wang, B., Cheng, Y., Gail, M. H., Fine, J. P., & Pfeiffer, R. M. (2024) · Statistical Methods in Medical Research · doi:10.1177/09622802241227945
Considerations for Enhancing Lung Cancer Risk Prediction and Screening in Asian Populations
ten Haaf, K. (2024) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2023.12.002
Nattino, G., Lemeshow, S. A., Carrara, G., Rossi, C., Brissy, O., Chieregato, A., Csomós, Á., Fleming, J. M., Giugni, A., Gradišek, P., et al. (2024) · Journal of Neurotrauma · doi:10.1089/neu.2023.0529
Bhattacharyay, S., van Leeuwen, F. D., Beqiri, E., Åkerlund, C. A. I., Wilson, L., Steyerberg, E. W., Nelson, D., Maas, A. I. R., Menon, D. K., & Ercole, A. (2024) · medRxiv · doi:10.1101/2024.05.14.24307364
Self-citationDevelopment and validation of a diagnostic nomogram for frailty in cancer patients
Luo, S., Chong, F., Huo, Z., Liu, J., Li, N., Lin, X., Yin, L., Song, C., Shi, H., & Xu, H. (2024) · Nutrition · doi:10.1016/j.nut.2024.112531
Lapi, F., Domnich, A., Marconi, E., Cricelli, I., Rossi, A., Icardi, G., & Cricelli, C. A. (2024) · Respiratory Medicine · doi:10.1016/j.rmed.2024.107761
Multicentre Validation of the RESECT-90 Prediction Model for 90-Day Mortality After Lung Resection
Taylor, M., Martin, G. P., Abah, U. D., Shackcloth, M. J., Granato, F., Booton, R., Coonar, A. S., & Grant, S. W. (2024) · Clinical Lung Cancer · doi:10.1016/j.cllc.2024.10.005
Collaborative, T. O., Williamson, E., Tazare, J., Bhaskaran, K., Walker, A. J., McDonald, H. I., Tomlinson, L. A., Bacon, S., Bates, C. J., Curtis, H. J., et al. (2024) · Wellcome Open Research · doi:10.12688/wellcomeopenres.16353.2
Self-citation
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.
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