Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement
Open accessAbstract
Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org ). Editors’ note: In order to encourage dissemination of the TRIPOD Statement, this article is freely accessible on the Annals of Internal Medicine Web site ( www.annals.org ) and will be also published in BJOG, British Journal of Cancer, British Journal of Surgery, BMC Medicine, British Medical Journal, Circulation, Diabetic Medicine, European Journal of Clinical Investigation, European Urology, and Journal of Clinical Epidemiology. The authors jointly hold the copyright of this article. An accompanying Explanation and Elaboration article is freely available only on www.annals.org ; Annals of Internal Medicine holds copyright for that article.
AI snapshot
Written from the paper’s full text.
TL;DR
The TRIPOD Statement is a reporting guideline consisting of a 22-item checklist for studies developing, validating, or updating multivariable prediction models for diagnosis or prognosis. It was developed through literature review, a Web-based survey, and a consensus meeting to address the generally poor reporting quality of prediction model studies.
Key findings
- The TRIPOD Statement is a checklist of 22 items deemed essential for transparent reporting of prediction model studies, covering title and abstract, background and objectives, methods, results, discussion, and other information.
- Items are marked as relevant to development (D), validation (V), or both (D;V); development-only items include 10a, 10b, 14, and 15, while validation-only items include 10c, 10e, 12, 13c, 17, and 19a.
- Cited reviews found poor reporting: Mallett and colleagues examined 47 reports of new cancer prediction models; Collins and colleagues evaluated 39 reports of type 2 diabetes prediction models; Bouwmeester and colleagues evaluated 71 reports published in 2008 in 6 high-impact general medical journals.
- TRIPOD is not intended for multivariable modeling in etiologic studies, studies investigating single prognostic factors, or impact studies quantifying the impact of using a prediction model.
Methods
- Guideline development followed published guidance for developing reporting guidelines and established a steering committee; a systematic search of MEDLINE, EMBASE, PsychINFO, and Web of Science generated 129 possible checklist items, which were merged into 76 candidate items.
- A Web-based survey rated the 76 candidate items; a 3-day meeting in June 2011 in Oxford, United Kingdom, was attended by 24 of 25 invited participants, 22 of whom had participated in the survey.
- The checklist was revised by the steering committee and circulated to participants; the final TRIPOD Statement is a 22-item checklist, accompanied by an explanation and elaboration document.
Limitations
- The paper states that TRIPOD is guidelines only for reporting and does not prescribe how to develop or validate a prediction model, nor is it a quality assessment tool to gauge model quality or quantify risk of bias.
- The paper states that methodological issues in developing, validating, and updating prediction models evolve, and TRIPOD will be periodically reappraised and modified if necessary.
- Evident from the text: the paper does not report an empirical evaluation of whether the TRIPOD checklist itself improves reporting quality or clinical use; it presents the development process and the checklist.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Claims the paper makes
0 of 40 cited sources checked · 0 supported · 0 flagged
- Premisecitation
Quantifying the predictive ability of a model on the same data from which it was developed tends to give an optimistic estimate of performance, owing to overfitting and use of predictor selection strategies.
“Quantifying the predictive ability of a model on the same data from which the model was developed (often referred to as apparent performance) will tend to give an optimistic estimate of performance, owing to overfitting (too few outcome events relative to the number of candidate predictors) and the use of predictor selection strategies [19].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [19] source
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- Premisecitation
Overfitting, optimism, and miscalibration may be addressed and accounted for during model development by applying shrinkage or penalization procedures.
“Overfitting, optimism, and miscalibration may also be addressed and accounted for during the model development by applying shrinkage (for example, heuristic or based on bootstrapping techniques) or penalization procedures (for example, ridge regression or lasso) [20].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [20] source
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- Premisecitation
In both the diagnostic and prognostic setting, estimates of probabilities are rarely based on a single predictor.
“In both the diagnostic and prognostic setting, estimates of probabilities are rarely based on a single predictor [5].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] source
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- Premisecitation
Internal validation is a necessary part of model development.
“Internal validation is a necessary part of model development [2].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [2]
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- Interpretationreasoning
None of the existing reporting guidelines (STROBE, REMARK, STARD, GRIPS) are entirely appropriate for prediction model studies.
“However, none of these guidelines are entirely appropriate for prediction model studies.”
Quote in the paperSubstantial claim
- Premisecitation
The quality of reporting of prediction model studies is poor.
“However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor.”
- Also stated in: § Reporting of multivariable prediction model studies · boosted
“These and other reviews provide a clear picture that, across different disease areas and different journals, there is a generally poor level of reporting of prediction model studies [6,2–27,29].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [6,2–27,29]
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- Also stated in: § Reporting of multivariable prediction model studies · boosted
- Interpretationreasoning
Very few prediction models, relative to the large number of models published, are widely implemented or used in clinical practice.
“It is therefore not surprising, and fortunate, that very few prediction models, relative to the large number of models published, are widely implemented or used in clinical practice [6].”
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- Interpretationreasoning
Such deficiencies ultimately lead to prediction models that are not or should not be used.
“Such deficiencies ultimately lead to prediction models that are not or should not be used.”
Quote in the paperSubstantial claim - Premisecitation
In 39 reports published before May 2011 describing development of models to predict prevalent or incident type 2 diabetes, reporting was generally poor, with key details on predictors, missing data, and model-building strategy often poorly described.
“Collins and colleagues [24] evaluated the methodological conduct and reporting of 39 reports published before May 2011 describing the development of models to predict prevalent or incident type 2 diabetes. Reporting was also found to be generally poor, with key details on which predictors were examined, the handling and reporting of missing data, and model-building strategy often poorly described.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [24] source
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- Premisecitation
In 71 reports published in 2008 in 6 high-impact general medical journals, the level of reporting was overwhelmingly poor.
“Bouwmeester and colleagues [23] evaluated 71 reports, published in 2008 in 6 high-impact general medical journals, and likewise observed an overwhelmingly poor level of reporting.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] source
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- Premisecitation
In the absence of detailed and transparent reporting of the key study details, it is difficult for the scientific and health care community to objectively judge the strengths and weaknesses of a prediction model study.
“In the absence of detailed and transparent reporting of the key study details, it is difficult for the scientific and health care community to objectively judge the strengths and weaknesses of a prediction model study [34,50,51].”
Quote in the paperNot a substantial claim0 of 3 sources checked - Premisenone given
Many prediction model studies are methodologically weak.
“In addition, because many such studies are methodologically weak, we also summarize the qualities of good (and the limitations of less good) studies, regardless of reporting [43].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [43] source
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- Premisecitation
The quality of reporting in published articles describing the development or validation of multivariable prediction models in medicine is poor.
“Many reviews have showed that the quality of reporting in published articles describing the development or validation of multivariable prediction models in medicine is poor [23–27,29].”
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- May combine two claims. 10
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InterpretationcitationThe TRIPOD Statement covers studies that report solely development, both development and external validation, and solely external validation (with or without updating), of a prediction model.
“The TRIPOD Statement covers studies that report solely development [12,15], both development and external validation, and solely external validation (with or without updating), of a prediction model [14] (Figure 1).”
Quote in the paperSubstantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningPrediction is inherently multivariable.
“Prediction is therefore inherently multivariable.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Interpretationnone givenThe TRIPOD Statement is a checklist of 22 items that the authors consider essential for good reporting of studies developing or validating multivariable prediction models.
“The TRIPOD Statement is a checklist of 22 items that we consider essential for good reporting of studies developing or validating multivariable prediction models (Table 1).”
Quote in the paperSubstantial claim- Rests on an assumption: The consensus process and expert opinion used to develop TRIPOD yield checklist items that are essential for transparent reporting of a prediction model study.
Classified as probably not a substantial claim.
PremisecitationIt is a general requirement of reporting of research that other researchers can replicate all steps taken and obtain the same results.
“It is a general requirement of reporting of research that other researchers can, if required, replicate all the steps taken and obtain the same results [32].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [32] source
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PremisereasoningThe focus of the REMARK checklist is primarily on prognostic factors and not prediction models, whereas the GRIPS statement is aimed at risk prediction using genetic risk factors and the specific methodological issues around handling large numbers of genetic variants.
“However, the focus of the REMARK checklist is primarily on prognostic factors and not prediction models, whereas the GRIPS statement is aimed at risk prediction using genetic risk factors and the specific methodological issues around handling large numbers of genetic variants.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationIn 47 reports published in 2005 presenting new prediction models in cancer, reporting was poor, with insufficient information in all aspects of model development.
“For example, Mallett and colleagues [26] examined 47 reports published in 2005 presenting new prediction models in cancer. Reporting was found to be poor, with insufficient information described in all aspects of model development, from descriptions of patient data to statistical modeling methods.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [26] source
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InterpretationreasoningKey details of how a prediction model was developed and validated must be clearly reported to enable synthesis and critical appraisal of all relevant information.
“It is therefore essential that key details of how a prediction model was developed and validated be clearly reported to enable synthesis and critical appraisal of all relevant information [14,33–36].”
Quote in the paperNot a substantial claim0 of 5 sources checked- cites [14] source
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- cites [33] source
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- cites [34] source
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- cites [35] source
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- cites [36]
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- Rests on an assumption: Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed.
Classified as probably not a substantial claim. May combine two claims.
InterpretationreasoningSome TRIPOD items are relevant only for studies reporting development of a prediction model (items 10a, 10b, 14, and 15), and others apply only to studies reporting external validation (items 10c, 10e, 12, 13c, 17, and 19a).
“Therefore, some items are relevant only for studies reporting the development of a prediction model (items 10a, 10b, 14, and 15), and others apply only to studies reporting the (external) validation of a prediction model (items 10c, 10e, 12, 13c, 17, and 19a).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
InterpretationcitationThe reporting of studies evaluating the incremental value of specific predictors, beyond established predictors or even beyond existing prediction models, fits entirely within the remit of TRIPOD.
“The reporting of studies evaluating the incremental value of specific predictors, beyond established predictors or even beyond existing prediction models [18,42], also fits entirely within the remit of TRIPOD (see the accompanying explanation and elaboration document [43]).”
Quote in the paperSubstantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataA consensus meeting was held in June 2011 with 24 of 25 invited participants, where each of the 76 candidate checklist items was discussed and consensus was reached on whether to retain, merge, or omit the item.
“The results of the survey were presented at a 3-day meeting in June 2011, in Oxford, United Kingdom; it was attended by 24 of the 25 invited participants (22 of whom had participated in the survey). During the 3-day meeting, each of the 76 candidate checklist items was discussed in turn, and a consensus was reached on whether to retain, merge with another item, or omit the item.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenWhile making revisions, conscious efforts were made to harmonize recommendations with other reporting guidelines, and where possible the same or similar wording for items was chosen.
“While making revisions, conscious efforts were made to harmonize our recommendations with other reporting guidelines, and where possible we chose the same or similar wording for items [37,39,41,45,46].”
Quote in the paperNot a substantial claim0 of 5 sources checkedClassified as probably not a substantial claim.
InterpretationreasoningAll other TRIPOD items are relevant to all types of prediction model development and validation studies.
“All other items are relevant to all types of prediction model development and validation studies.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataTwenty-five experts with a specific interest in prediction models were invited to participate in a Web-based survey and rate the importance of the 76 candidate checklist items; respondents included methodologists, health care professionals, and journal editors.
“Twenty-five experts with a specific interest in prediction models were invited by e-mail to participate in the Web-based survey and to rate the importance of the 76 candidate checklist items. Respondents (24 of 27) included methodologists, health care professionals, and journal editors.”
Quote in the paperNot a substantial claim- Rests on an assumption: The 25 invited experts and meeting participants are representative of the relevant expertise for developing a reporting guideline for prediction models.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenAfter the meeting, the checklist was revised by the steering committee during numerous face-to-face meetings and circulated to participants to ensure it reflected the discussions.
“After the meeting, the checklist was revised by the steering committee during numerous face-to-face meetings, and circulated to the participants to ensure it reflected the discussions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisereasoningThe 2 guidelines most closely related to prediction models are REMARK and GRIPS.
“The 2 guidelines most closely related to prediction models are REMARK and GRIPS.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataA list of 129 possible checklist items was generated from the identified studies, and the steering committee merged related items to create a list of 76 candidate items.
“From these studies, a list of 129 possible checklist items was generated. The steering committee then merged related items to create a list of 76 candidate items.”
Quote in the paperNot a substantial claim- Rests on an assumption: The literature search and item reduction process adequately captured all relevant reporting recommendations for prediction models.
Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenTRIPOD places much more emphasis on validation studies and the reporting requirements for such studies.
“TRIPOD also places much more emphasis on validation studies and the reporting requirements for such studies.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationThe TRIPOD Statement was developed following published guidance for developing reporting guidelines.
“We followed published guidance for developing reporting guidelines [44] and established a steering committee (Drs. Collins, Reitsma, Altman, and Moons) to organize and coordinate the development of TRIPOD.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [44] source
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Interpretationnone givenTRIPOD explicitly covers the development and validation of prediction models for both diagnosis and prognosis, for all medical domains and all types of predictors.
“TRIPOD explicitly covers the development and validation of prediction models for both diagnosis and prognosis, for all medical domains and all types of predictors.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAn ever-increasing number of studies are evaluating the incremental value of specific predictors, beyond established predictors or even beyond existing prediction models.
“An ever-increasing number of studies are evaluating the incremental value of specific predictors, beyond established predictors or even beyond existing prediction models [18,42].”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenA comprehensive evidence base from existing systematic reviews of prediction models was used to support and justify the rationale for including and illustrating each checklist item.
“A comprehensive evidence base from existing systematic reviews of prediction models was used to support and justify the rationale for including and illustrating each checklist item.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe explanation and elaboration document was developed through several face-to-face meetings, teleconferences, and iterations among the authors, with additional revisions after sharing with the whole TRIPOD group before final approval.
“The development of the explanation and elaboration document was completed after several face-to-face meetings, teleconferences, and iterations among the authors. Additional revisions were made after sharing the document with the whole TRIPOD group before final approval.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
PremisecitationThe importance of publishing protocols for developing or validating prediction models, certainly when conducting a prospective study, is slowly being acknowledged.
“The importance of also publishing protocols for developing or validating prediction models, certainly when conducting a prospective study, is slowly being acknowledged [55,56].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [55]
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- cites [56]
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The paper relies on this without support.
Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed.
“Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed.”
The paper relies on this without support.
The consensus process and expert opinion used to develop TRIPOD yield checklist items that are essential for transparent reporting of a prediction model study.
“The TRIPOD Statement is a checklist of 22 items that we consider essential for good reporting of studies developing or validating multivariable prediction models (Table 1).”
The paper relies on this without support.
The 25 invited experts and meeting participants are representative of the relevant expertise for developing a reporting guideline for prediction models.
“Twenty-five experts with a specific interest in prediction models were invited by e-mail to participate in the Web-based survey and to rate the importance of the 76 candidate checklist items.”
The paper relies on this without support.
The literature search and item reduction process adequately captured all relevant reporting recommendations for prediction models.
“From these studies, a list of 129 possible checklist items was generated. The steering committee then merged related items to create a list of 76 candidate items.”
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?
Checklists and model cards are one way to provide standard best practices for reporting details about ML models.
“Specifically, checklists and model cards are one way to provide standard best practices for reporting details about ML models.38,39,40”
From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [39]
The Discussion describes TRIPOD as a checklist intended to improve reporting of prediction-model studies and specifies the information authors should report. Our copy does not discuss model cards, and its guidance concerns medical prediction models rather than ML models generally. (LLM, conf=0.84)
Experimental — accuracy not yet measuredCurrent standards for reporting model performance in ML-based science often fall short in addressing issues caused by leakage.
“Current standards for reporting model performance in ML-based science often fall short in addressing issues caused by leakage.”
From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [39]
The Background sections on reporting and reporting guidelines say that prediction-model studies are often poorly reported and that existing guidelines are not entirely appropriate for such studies. They do not say that reporting standards fail to address leakage, or assess standards for ML-based science broadly. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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.
Prediction-study types are attributed to the wrong figure
MinorThe text cites Figure 1 for the types of prediction-model studies covered by TRIPOD, but the paper labels Figure 3 as showing those types. Figure 1 instead depicts diagnostic and prognostic prediction modeling.
“The TRIPOD Statement covers studies that report solely development [12,15], both development and external validation, and solely external validation (with or without updating), of a prediction model [14] (Figure 1).”
“Figure: Figure 3 Types of prediction model studies covered by the TRIPOD statement. D = development data; V = validation data.”
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Self-citationPrognosis and prognostic research: validating a prognostic model
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Self-citationEurp-pean system for cardiac operative risk evaluation (Euro SCORE)
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Self-citationA simplified acute physiology score for ICU patients
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“These and other reviews provide a clear picture that, across different disease areas and different journals, there is a generally poor level of reporting of prediction model studies [6,2-27,29].”
InfluentialSelf-citationPrognosis and prognostic research: Developing a prognostic model
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Self-citationAssessing the Generalizability of Prognostic Information
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“in a similar style to those for other reporting guidelines [47-49].”
Self-citationPrognosis and prognostic research: application and impact of prognostic models in clinical practice
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Self-citationPrognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research
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“In both the diagnostic and prognostic setting, estimates of probabilities are rarely based on a single predictor (5).”
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“These and other reviews provide a clear picture that, across different disease areas and different journals, there is a generally poor level of reporting of prediction model studies [6,2-27,29].”
InfluentialA prognostic index in primary breast cancer.
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“These and other reviews provide a clear picture that, across different disease areas and different journals, there is a generally poor level of reporting of prediction model studies [6,2-27,29].”
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“Several reviews have evaluated the quality of published reports that describe the development or validation prediction models [23-28].”
Self-citationA study to develop clinical decision rules for the use of radiography in acute ankle injuries
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“These and other reviews provide a clear picture that, across different disease areas and different journals, there is a generally poor level of reporting of prediction model studies [6,2-27,29].”
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Self-citationReporting and Methods in Clinical Prediction Research: A Systematic Review
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“These and other reviews provide a clear picture that, across different disease areas and different journals, there is a generally poor level of reporting of prediction model studies [6,2-27,29].”
InfluentialSelf-citationCollins, G. S., Mallett, S., Omar, O., & Yu, L.-M. (2011) · BMC Medicine · doi:10.1186/1741-7015-9-103 · Cited by 484 · cited 5× · see citing works
“These and other reviews provide a clear picture that, across different disease areas and different journals, there is a generally poor level of reporting of prediction model studies [6,2-27,29].”
InfluentialSelf-citationReporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration
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Self-citationComparisons of established risk prediction models for cardiovascular disease: systematic review
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“clearly reported to enable synthesis and critical appraisal of all relevant information [14,33-36].”
Assessing the incremental value of diagnostic and prognostic markers: a review and illustration
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Reproducible Research: Moving toward Research the Public Can Really Trust
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“can, if required, replicate all the steps taken and obtain the same results [32].”
Reporting methods in studies developing prognostic models in cancer: a review
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“For example, Mallett and colleagues [26] examined 47 reports published in 2005 presenting new prediction models in cancer.”
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Self-citationDorresteijn, J. A. N., Visseren, F. L. J., Ridker, P. M., Wassink, A. M. J., Paynter, N. P., Steyerberg, E. W., van der Graaf, Y., & Cook, N. R. (2011) · BMJ · doi:10.1136/bmj.d5888 · Cited by 177 · cited 2× · see citing works
“These and other reviews provide a clear picture that, across different disease areas and different journals, there is a generally poor level of reporting of prediction model studies [6,2-27,29].”
Ten steps towards improving prognosis research
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“gov).(53,54) The importance of also publishing protocols for developing or validating prediction models, certainly when conducting a prospective study, is slowly being acknowledged.”
Self-citationRegistration of observational studies: Is it time?
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Self-citationApplication of Shrinkage Techniques in Logistic Regression Analysis: A Case Study
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“These and other reviews provide a clear picture that, across different disease areas and different journals, there is a generally poor level of reporting of prediction model studies [6,2-27,29].”
Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs)
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“External validation may use participant data collected by the same investigators, typically using the same predictor and outcome definitions and measurements, but sampled from a later period (temporal or narrow validation); by other investigators in another hospital or country, sometimes using different definitions an…”
Self-citationComparing risk prediction models
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“clearly reported to enable synthesis and critical appraisal of all relevant information [14,33-36].”
Self-citationRisk assessment of fragility fractures: summary of NICE guidance
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“The reporting of studies evaluating the incremental value of specific predictors, beyond established predictors or even beyond existing prediction models [18,42], also fits entirely within the remit of TRIPOD (see the accompanying”
Canadian C-Spine Rule study for alert and stable trauma patients: I. Background and rationale
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DIAGNOSTIC PREDICTION RULES: PRINCIPLES, REQUIREMENTS, AND PITFALLS
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“In the absence of detailed and transparent reporting of the key study details, it is difficult for the scientific and health care community to objectively judge the strengths and weaknesses of a prediction model study [34,50,51].”
Self-citationDeveloping and Evaluating Prediction Models in Rehabilitation Populations
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“In the absence of detailed and transparent reporting of the key study details, it is difficult for the scientific and health care community to objectively judge the strengths and weaknesses of a prediction model study [34,50,51].”
Strengthening the reporting of genetic risk prediction studies: the GRIPS statement
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Conflicts at the heart of the FRAX tool
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Strengthening the reporting of genetic risk prediction studies: the GRIPS statement
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Canadian C-Spine Rule study for alert and stable trauma patients: I. Background and rationale.
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- Authors
- Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K.
- Affiliations
- University Medical Center Utrecht (NL)
- University of Oxford (GB)
- Venue
- BMC Medicine
- Published
- 2015
- Volume
- 13
- Issue
- 1
- Pages
- 1
- ISSN
- 1741-7015 (ISSN-L)
- PMC
- PMC4284921
- OpenAlex
- W2019694480
- Semantic Scholar
- 77e57b765074ac83f1053089440ab39c25fb8fa0
- Cited by
- 1,927
- References
- 71
- Citations by year
- 2026: 213 · 2025: 303 · 2024: 330 · 2023: 295 · 2022: 236 · 2021: 178 · 2020: 110 · 2019: 101 · 2018: 50 · 2017: 60 · 2016: 42 · 2015: 6 · 2013: 1
- FWCI
- 47.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Birmingham Research Portal (University of Birmingham) (acceptedVersion, cc-by)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Funders
- Cancer Research UK (16895, C5529)
- National Institute for Health and Care Research (NF-SI-0508-10259, NF-SI-0513-10131)
- ZonMw (91208004)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (918.10.615)
- Medical Research Council
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Clinical practice guidelines implementation
- Keywords
- TRIPOD statement, multivariable prediction models, reporting quality, diagnostic model, prognostic model, reporting checklist, risk of bias, prediction model validation, clinical decision-making
- MeSH terms
- Diagnosis, Humans, Models, Theoretical, Prognosis, Multivariate Analysis, Guidelines as Topic, Risk Assessment, Checklist
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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.
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 ›
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