TRIPOD Checklist Checker
Check your clinical prediction model study against the TRIPOD reporting checklist before submission.
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What the TRIPOD checklist requires
TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) is the EQUATOR-endorsed reporting guideline for studies that develop or validate clinical prediction models. TRIPOD covers the title and abstract, introduction, methods (data source, participants, outcome, predictors, sample size, missing data, statistical analysis, model development and validation), results (participants, model specification, model performance), discussion, and other information. The TRIPOD+AI extension (2024) adds items for prediction models built with machine learning. CheckMyManuscript flags missing or structurally incomplete TRIPOD items, such as undefined predictors, no sample-size justification, or missing calibration and discrimination results, so you can fix them before submission. It checks presence and structure, not methodological adequacy.
TRIPOD items, and what CMM checks
Each item below maps to TRIPOD (and TRIPOD+AI for ML models). CMM flags presence and structural signals; methodological adequacy is editorial and needs human review.
Title & abstract
✓ CMM checks thisIdentifies the study as developing and/or validating a prediction model; abstract reports the model and its performance.
Flag: title does not state development vs validation.
Source: tripod-statement.org · verified Jun 16, 2026
Data source, participants & outcome
✓ CMM checks thisSource of data and study design, eligibility, and the outcome the model predicts with how/when it was measured.
Source: tripod-statement.org · verified Jun 16, 2026
Predictors & sample size
✓ CMM checks thisAll candidate predictors with how/when measured, and how study size was arrived at.
Flag: predictors not defined, or no sample-size justification (e.g. events per variable).
Source: tripod-statement.org · verified Jun 16, 2026
Missing data & analysis methods
✓ CMM checks thisHow missing data were handled and the statistical methods for developing or validating the model.
Source: tripod-statement.org · verified Jun 16, 2026
Model performance: calibration & discrimination
✓ CMM checks thisModel performance measures including calibration and discrimination (e.g. C-statistic), with the full model presented.
Flag: discrimination reported but no calibration; or model not fully specified for use.
Source: tripod-statement.org · verified Jun 16, 2026
TRIPOD+AI items (for ML models)
✓ CMM checks thisFor machine-learning models: reporting of data handling, model type, and fairness/transparency items per TRIPOD+AI.
Source: tripod-statement.org · verified Jun 16, 2026
Model validity
Editorial, not auto-checkableWhether the model is sufficiently validated and clinically useful.
Editorial judgement, outside the scope of an automated checker.
Source: equator-network.org · verified Jun 16, 2026
This page reflects the TRIPOD statement for prediction model studies, with the TRIPOD+AI (2024) extension for studies using machine learning. CheckMyManuscript checks for the presence, structure, and likely-completeness signals of each item; it does not assess methodological quality and does not replace peer review. Verify the live checklist before submitting, last checked 16 June 2026.
TRIPOD is the reporting standard for clinical prediction model studies, with the TRIPOD+AI (2024) extension for models built using machine learning. Journals expect TRIPOD-compliant reporting for prognostic and diagnostic prediction models. CheckMyManuscript screens your manuscript for the presence and structure of each TRIPOD item so preventable gaps are caught before submission.
TRIPOD and TRIPOD+AI
The base TRIPOD statement covers development and validation of multivariable prediction models. TRIPOD+AI (2024) extends it with reporting items specific to machine-learning models, including data handling, model type, and fairness and transparency. If your model uses ML methods, report against TRIPOD+AI as well as the core items.
What CheckMyManuscript checks, and what it does not
CheckMyManuscript flags structural signals: undefined predictors or outcome, no sample-size justification, unreported missing-data handling, or model performance that omits calibration or discrimination. It does not judge whether the modelling approach was appropriate, whether overfitting was controlled, or whether the model is clinically useful. Those are methodological judgements for peer and editorial review. Use the checker as a pre-submission completeness screen, not a compliance certificate.
Also see: STARD checklist checker | STROBE checklist checker | Medical papers checker
TRIPOD-specific checks
Development vs validation
Flags if the title/abstract do not state model development or validation.
Predictors & outcome
Flags undefined predictors or outcome.
Sample size
Flags a missing sample-size justification.
Missing data
Flags if missing-data handling is not reported.
Calibration & discrimination
Flags if model performance omits calibration or discrimination.
TRIPOD+AI items
Flags ML-specific reporting gaps for machine-learning models.
Checks relevant to this topic
Part of our 80+ automated checks
Development/validation stated
Study type stated in title/abstract.
Predictors defined
Candidate predictors reported.
Sample size justified
Sample-size rationale reported.
Calibration reported
Model calibration reported.
Discrimination reported
Model discrimination (e.g. C-statistic) reported.
The practical edge your peers already use
Across disciplines and career stages, researchers reduce bottlenecks and submit with confidence: clearer drafts, easier guideline compliance, and less back and forth with co‑authors and reviewers.
I use it to review my students' papers. It instantly highlights typos, missing references, and unclear sections, helping me focus my feedback on the quality of the research instead of surface errors.
Ilyass
Professor in Mechanical Engineering, ÉTS Montréal
I relied on it throughout my thesis to strengthen my writing. It suggested clearer phrasing, improved flow between sections, and ensured my references were complete before the final deadline.
Manon
Master's Student in Speech Therapy
I write research in both Portuguese and English, and it adapts perfectly to either language. It provided precise feedback in Portuguese, helping me maintain academic tone and consistency across my drafts.
Afonso
PhD Candidate, UFPE
It gave excellent advice on how to rephrase and present ideas more clearly and concisely. The suggestions helped me refine my arguments and make my research more impactful.
Félix
Postdoc Researcher, Max Planck Institute for Evolutionary Biology
A round of suggestions helped to generally refine the text of my paper and, moreover, to present some of its key points in a more focused form.
Oleg
Professor, Pirogov Russian National Research Medical University
I use it to review my students' papers. It instantly highlights typos, missing references, and unclear sections, helping me focus my feedback on the quality of the research instead of surface errors.
Ilyass
Professor in Mechanical Engineering, ÉTS Montréal
I relied on it throughout my thesis to strengthen my writing. It suggested clearer phrasing, improved flow between sections, and ensured my references were complete before the final deadline.
Manon
Master's Student in Speech Therapy
I write research in both Portuguese and English, and it adapts perfectly to either language. It provided precise feedback in Portuguese, helping me maintain academic tone and consistency across my drafts.
Afonso
PhD Candidate, UFPE
It gave excellent advice on how to rephrase and present ideas more clearly and concisely. The suggestions helped me refine my arguments and make my research more impactful.
Félix
Postdoc Researcher, Max Planck Institute for Evolutionary Biology
A round of suggestions helped to generally refine the text of my paper and, moreover, to present some of its key points in a more focused form.
Oleg
Professor, Pirogov Russian National Research Medical University
Frequently asked questions
Use the base TRIPOD statement for regression-based prediction models. If your model uses machine learning, also report against TRIPOD+AI (2024), which extends TRIPOD with items specific to machine-learning methods, including data handling, model type, and fairness/transparency. Confirm which your target journal expects.
No. CheckMyManuscript checks the presence, structure, and likely-completeness signals of TRIPOD items, for example, whether predictors and outcome are defined, a sample-size rationale exists, and calibration and discrimination are reported. It does not judge whether the model is methodologically sound or clinically useful, and it does not replace peer review.
TRIPOD asks for both because they describe different aspects of a prediction model: discrimination (e.g. the C-statistic) measures how well the model separates outcomes, while calibration measures how well predicted risks match observed risks. Reporting only one is a common gap CheckMyManuscript can flag.