CoChat
Sign Up
Cited by 793Meta-analysis and systematic reviewsStatistical Methods in EpidemiologyClinical practice guidelines implementation

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement

Open access
Collins, Gary S., Reitsma, Johannes B., Altman, Douglas G., Moons, Karel G.M.·2015·Circulation
DOI 10.1161/circulationaha.114.014508PMC PMC4297220
Open this paper in CoChat — free accountOpen PDF

Abstract

Background—: 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. Methods—: 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. Results—: 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. Conclusions—: 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 ).

AI snapshot

Written from the paper’s full text.

TL;DR

The TRIPOD Statement is a 22-item reporting checklist for studies developing, validating, or updating multivariable prediction models for diagnosis or prognosis, developed through a systematic literature review, a web-based expert survey, and a consensus meeting. It is intended to improve the transparency of reporting, not to prescribe how models should be built or to serve as a quality-assessment tool.

Key findings

  • The final TRIPOD Statement comprises a checklist of 22 items deemed essential for transparent reporting of prediction model studies, covering title/abstract (items 1–2), background and objectives (item 3), methods (items 4–12), results (items 13–17), discussion (items 18–20), and other information (items 21–22).
  • Items are tagged as relevant to development only (items 10a, 10b, 14, 15; denoted D), validation only (items 10c, 10e, 12, 13c, 17, 19a; denoted V), or both (D;V).
  • The development process began with a systematic search yielding a list of 129 possible checklist items, which the steering committee merged into 76 candidate items; 24 of 27 invited respondents completed the web-based survey rating item importance.
  • The paper cites prior reviews showing poor reporting: Mallett et al. examined 47 reports of new cancer prediction models published in 2005; Collins et al. evaluated 39 reports of type 2 diabetes models published before May 2011; Bouwmeester et al. evaluated 71 reports published in 2008 in 6 high-impact general medical journals — all finding generally poor reporting.

Methods

  • Guideline development followed published guidance for developing reporting guidelines, with a steering committee (Collins, Reitsma, Altman, Moons) coordinating the process.
  • A systematic search of MEDLINE, EMBASE, PsychINFO, and Web of Science identified articles making reporting recommendations for multivariable prediction models, reviews of methodological conduct/reporting, and reviews of multivariable models generally; these generated 129 candidate items, reduced to 76.
  • Twenty-five experts were invited by e-mail to rate the 76 candidate items in a web-based survey (24 of 27 respondents, including 2 additional statistical editors from Annals of Internal Medicine); results were presented at a 3-day consensus meeting in June 2011 in Oxford, attended by 24 of 25 invited participants (22 of whom had completed the survey), after which the checklist was revised by the steering committee and circulated to participants.

Limitations

  • Stated by the paper: TRIPOD is a reporting guideline only 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; it is not intended for multivariable modeling in etiologic studies, studies of single prognostic factors, or impact studies of model use on behavior, outcomes, or cost-effectiveness.
  • Stated by the paper: methodological issues in prediction modeling evolve, so TRIPOD will be periodically reappraised and modified as needed.
  • Evident from the text: the checklist items themselves are presented only in a referenced Table that is not reproduced in the article body, and the paper reports no empirical evaluation of whether adopting TRIPOD improves reporting quality.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1161/circulationaha.114.014508

Cites

68 of 68 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 40 cited sources checked · 0 supported · 0 flagged

  • Premisecitation

    The quality of reporting of prediction model studies is generally 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.6,23–27,29”
    Quote in the paperSubstantial claim0 of 7 sources checked
    • cites [6] Steyerberg EW Moons KG van der Windt DA Hayden JA Perel P Schroter S PROGRESS Group Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 2013 10 e1001381 23393430 PMC3564751 source

      Not checked in the free run.

    • cites [23] Bouwmeester W Zuithoff NP Mallett S Geerlings MI Vergouwe Y Steyerberg EW Altman DG Moons KG Reporting and methods in clinical prediction research: a systematic review. PLoS Med 2012 9 1 12 doi: 10.1371/journal.pmed.1001221. 22629234 PMC3358324 source

      Not checked in the free run.

    • cites [24] Collins GS Mallett S Omar O Yu LM Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med 2011 9 103 doi: 10.1186/1741-7015-9-103. 21902820 PMC3180398 source

      Not checked in the free run.

    • cites [25] Collins GS Omar O Shanyinde M Yu LM A systematic review finds prediction models for chronic kidney were poorly reported and often developed using inappropriate methods. J Clin Epidemiol 2013 66 268 77 23116690 source

      Not checked in the free run.

    • cites [26] Mallett S Royston P Dutton S Waters R Altman DG Reporting methods in studies developing prognostic models in cancer: a review. BMC Med 2010 8 20 20353578 PMC2856521 source

      Not checked in the free run.

    • cites [27] Laupacis A Sekar N Stiell IG Clinical prediction rules. A review and suggested modifications of methodological standards. JAMA 1997 277 488 494 9020274 source

      Not checked in the free run.

    • cites [29] Ettema RG Peelen LM Schuurmans MJ Nierich AP Kalkman CJ Moons KG Prediction models for prolonged intensive care unit stay after cardiac surgery: systematic review and validation study. Circulation 2010 122 682 9 7 p following p 689. doi: 10.1161/CIRCULATIONAHA.109.926808. 20679549 source

      Not checked in the free run.

  • Premisereasoning

    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”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [6] Steyerberg EW Moons KG van der Windt DA Hayden JA Perel P Schroter S PROGRESS Group Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 2013 10 e1001381 23393430 PMC3564751 source

      Not checked in the free run.

  • Premisecitation

    In both diagnostic and prognostic settings, 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] Riley RD Hayden JA Steyerberg EW Moons KG Abrams K Kyzas PA Malats N Briggs A Schroter S Altman DG Hemingway H PROGRESS Group Prognosis Research Strategy (PROGRESS) 2: prognostic factor research. PLoS Med 2013 10 e1001380 doi: 10.1371/journal.pmed.1001380. 23393429 PMC3564757 source

      Not checked in the free run.

  • Premisereasoning

    None of the existing reporting guidelines for observational, tumor marker, diagnostic accuracy, and genetic risk prediction studies are entirely appropriate for prediction model studies.

    “However, none of these guidelines are entirely appropriate for prediction model studies.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [39] von Elm E Altman DG Egger M Pocock SJ Gøtzsche PC Vandenbroucke JP STROBE Initiative Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ 2007 335 806 808 doi: 10.1136/bmj.39335.541782.AD. 17947786 PMC2034723 source

      Not checked in the free run.

    • cites [37] McShane LM Altman DG Sauerbrei W Taube SE Gion M Clark GM Statistics Subcommittee of the NCI-EORTC Working Group on Cancer Diagnostics Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst 2005 97 1180 1184 doi: 10.1093/jnci/dji237. 16106022 source

      Not checked in the free run.

    • cites [40] Bossuyt PM Reitsma JB Bruns DE Gatsonis CA Glasziou PP Irwig LM Lijmer JG Moher D Rennie D de Vet HC Standards for Reporting of Diagnostic Accuracy Towards complete and accurate reporting of studies of diagnostic accuracy: The STARD Initiative. Radiology 2003 226 24 28 doi: 10.1148/radiol.2261021292. 12511664 source

      Not checked in the free run.

  • 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] Steyerberg EW Eijkemans MJ Habbema JD Application of shrinkage techniques in logistic regression analysis: a case study. Statistica Neerlandica 2001 55 76 88 source

      Not checked in the free run.

  • Classified as probably not a substantial claim. 14
  • May combine two claims. 6
  • The quote only partly states this claim. 1

  • Classified as probably not a substantial claim.

    Findingcitation

    The 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 model14 (Figure 3).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [12] Royston P Moons KG Altman DG Vergouwe Y Prognosis and prognostic research: Developing a prognostic model. BMJ 2009 338 b604 19336487 source

      Not checked in the free run.

    • cites [15] Moons KG Kengne AP Woodward M Royston P Vergouwe Y Altman DG Grobbee DE Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart 2012 98 683 690 doi: 10.1136/heartjnl-2011-301246. 22397945 source

      Not checked in the free run.

    • cites [14] Altman DG Vergouwe Y Royston P Moons KG Prognosis and prognostic research: validating a prognostic model. BMJ 2009 338 b605 19477892 source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Findingnone given

    The 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 resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study.”
    • Also stated in: § TRIPOD Components · plain
      “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).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Expert consensus and a Web-based survey of prediction model researchers identify checklist items that are essential for transparent reporting.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    TRIPOD 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 claim
    • Rests on an assumption: A reporting checklist improves the transparency of reporting of prediction model studies.

  • Classified as probably not a substantial claim. May combine two claims. The quote only partly states this claim.

    Findingnone given

    TRIPOD 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 claim
    • Rests on an assumption: A reporting checklist improves the transparency of reporting of prediction model studies.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    It is 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.

    “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] Altman DG Vergouwe Y Royston P Moons KG Prognosis and prognostic research: validating a prognostic model. BMJ 2009 338 b605 19477892 source

      Not checked in the free run.

    • cites [33] Siontis GC Tzoulaki I Siontis KC Ioannidis JP Comparisons of established risk prediction models for cardiovascular disease: systematic review. BMJ 2012 344 e3318 22628003 source

      Not checked in the free run.

    • cites [34] Seel RT Steyerberg EW Malec JF Sherer M Macciocchi SN Developing and evaluating prediction models in rehabilitation populations. Arch Phys Med Rehabil 2012 93 8 Suppl S138 S153 doi: 10.1016/j.apmr.2012.04.021. 22840880 source

      Not checked in the free run.

    • cites [35] Collins GS Moons KG Comparing risk prediction models. BMJ 2012 344 e3186 22628131 source

      Not checked in the free run.

    • cites [36] Knottnerus JA Diagnostic prediction rules: principles, requirements and pitfalls. Prim Care 1995 22 341 363 7617791 source

      Not checked in the free run.

    • 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.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    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.

    “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 claim
    • Rests on an assumption: Expert consensus and a Web-based survey of prediction model researchers identify checklist items that are essential for transparent reporting.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Prediction is inherently multivariable.

    “Prediction is therefore inherently multivariable.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    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 paperNot a substantial claim0 of 1 sources checked
    • cites [41] Janssens AC Ioannidis JP van Duijn CM Little J Khoury MJ GRIPS Group Strengthening the reporting of genetic risk prediction studies: the GRIPS statement. Eur J Clin Invest 2011 41 1004 1009 doi: 10.1111/j.1365-2362.2011.02494.x. 21434891 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Mallett and colleagues examined 47 reports published in 2005 presenting new prediction models in cancer and found reporting to be poor, with insufficient information described in all aspects of model development.

    “For example, Mallett and colleagues26 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] Mallett S Royston P Dutton S Waters R Altman DG Reporting methods in studies developing prognostic models in cancer: a review. BMC Med 2010 8 20 20353578 PMC2856521 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Collins and colleagues evaluated 39 reports published before May 2011 describing the development of models to predict prevalent or incident type 2 diabetes and found reporting generally poor, with key details often poorly described.

    “Collins and colleagues24 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] Collins GS Mallett S Omar O Yu LM Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med 2011 9 103 doi: 10.1186/1741-7015-9-103. 21902820 PMC3180398 source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    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] Steyerberg EW Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating 2009 New York Springer source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Premisecitation

    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.

    “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 paperNot a substantial claim0 of 1 sources checked
    • cites [32] Laine C Goodman SN Griswold ME Sox HC Reproducible research: moving toward research the public can really trust. Ann Intern Med 2007 146 450 453 17339612 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The TRIPOD checklist was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors.

    “The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Expert consensus and a Web-based survey of prediction model researchers identify checklist items that are essential for transparent reporting.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    The TRIPOD checklist 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.

    “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.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Expert consensus and a Web-based survey of prediction model researchers identify checklist items that are essential for transparent reporting.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    The development of the TRIPOD Statement followed published guidance for developing reporting guidelines.

    “We followed published guidance for developing reporting guidelines44 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] Moher D Schulz KF Simera I Altman DG Guidance for developers of health research reporting guidelines. PLoS Med 2010 16 e1000217 20169112 PMC2821895 source

      Not checked in the free run.

    • Rests on an assumption: Expert consensus and a Web-based survey of prediction model researchers identify checklist items that are essential for transparent reporting.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    The funding sources had no role in the study design, data collection, analysis, preparation of the manuscript, or decision to submit the manuscript for publication.

    “The funding sources had no role in the study design, data collection, analysis, preparation of the manuscript, or decision to submit the manuscript for publication.”
    Quote in the paperNot a substantial claim
16 of 16

  • 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.

    Expert consensus and a Web-based survey of prediction model researchers identify checklist items that are essential for transparent reporting.

    “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.”

  • The paper relies on this without support.

    A reporting checklist improves the transparency of reporting of prediction model studies.

    “The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used.”
3 of 3

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?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M.
Affiliations
  • From Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, United Kingdom, and Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. The current affiliation for Drs Collins and Altman is the Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre,...
  • University Medical Center Utrecht (NL)
ORCID
Venue
Circulation
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2015-01-13
Volume
131
Issue
2
Pages
211-219
ISSN
  • 0009-7322 (ISSN-L)
  • 1524-4539
DOI
10.1161/circulationaha.114.014508
PMC
PMC4297220
OpenAlex
W2106671991
Cited by
793
References
68
Citations by year
2026: 114 · 2025: 138 · 2024: 138 · 2023: 91 · 2022: 98 · 2021: 108 · 2020: 54 · 2019: 17 · 2018: 6 · 2017: 15 · 2016: 9 · 2015: 5
FWCI
10.64
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Circulation (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
  • University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
Funders
  • Vanderbilt University
  • Memorial Sloan-Kettering Cancer Center
  • Cleveland Clinic
  • University of Ottawa
  • Cancer Research UK (16895)
  • National Institute for Health and Care Research (NF-SI-0508-10259, NF-SI-0513-10131)
  • University College London
  • University of Oxford
  • ZonMw (91208004)
  • Universiteit Maastricht
  • Albert-Ludwigs-Universität Freiburg
  • Nederlandse Organisatie voor Wetenschappelijk Onderzoek (918.10.615)
  • University of North Carolina at Chapel Hill
  • 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, transparency, prognostic model, diagnostic model, reporting checklist, risk of bias, prediction model validation, clinical decision-making
MeSH terms
Diagnosis, Humans, Models, Theoretical, Prognosis, Consensus, Validation Studies as Topic, 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 2026-10-10.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.

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