Prediction Model Study Report Template (TRIPOD+AI)

Prediction Model Study Report: [Model name and outcome]

Report template for studies that develop a clinical prediction model, evaluate (validate) an existing one, or do both, whether the model uses regression or machine learning. The sections follow the 27 items of the TRIPOD+AI statement (BMJ 2024), which replaced the TRIPOD 2015 checklist, written in original words for this collection. Use it with the official TRIPOD+AI checklist and the TRIPOD+AI for Abstracts checklist from tripod-statement.org. This is a reporting format, not clinical, statistical or regulatory advice; check the current official checklist and involve a qualified statistician. Replace every [bracketed] value and every row marked Example.

Document control

Approvals

Revision history

Contents

1. Title and abstract
2. Introduction
3. Methods
4. Open science
5. Patient and public involvement
6. Results
7. Discussion
8. TRIPOD+AI item map
9. Glossary and references
10. Standard and licence note

1. Title and abstract

The title names the study type, the population and the predicted outcome so that indexers and readers can find it. The abstract is structured and self-contained.

Title: [Development and external evaluation] of a [regression / machine learning] model to predict [outcome] in [population]

Example: Development and temporal external evaluation of a penalised logistic regression model to predict acute kidney injury within 7 days of major non-cardiac surgery in adults.

2. Introduction

The introduction explains the clinical need, the intended use of the model and what the study sets out to do.

2.1 Background and intended use (items 3a to 3c)

[Describe the healthcare context, the reason for developing or evaluating a model, and existing models with their limits. Cite a systematic review of existing models where one exists.]

2.2 Objectives (item 4)

3. Methods

This section describes the data, the participants, the outcome and predictors, and every analytic step from raw data to final model.

3.1 Data sources (items 5a and 5b)

Say whether the data are representative of the target population and the setting in which the model will be used.

3.2 Participants (items 6a to 6c)

[Describe the setting (primary, secondary or tertiary care, number and location of centres), eligibility criteria, and any treatment received that could affect the outcome.]

3.3 Data preparation (item 7)

Describe pre-processing and data quality checks, such as unit harmonisation, outlier handling and how similar the steps were across sociodemographic groups. Example: creatinine values in µmol/L and mg/dL were harmonised to µmol/L; values outside 20 to 2,000 µmol/L were treated as entry errors and set to missing.

3.4 Outcome (items 8a to 8c)

3.5 Predictors (items 9a to 9c)

List every candidate predictor, how and when it was measured, and why it was chosen. Say whether predictor assessment was blinded to the outcome.

3.6 Sample size (item 10)

Explain how the study size was reached, for development and for evaluation separately.

Example: the development data contained 4,812 patients and 386 events (8.0%), which exceeds the requirement. For evaluation, aim for enough events to estimate the calibration slope and the observed to expected ratio precisely; state the target and the method used.

3.7 Missing data (item 11)

State how missing values were handled (complete case, single or multiple imputation) and why, and the imputation model. Example: multiple imputation by chained equations, 20 imputed datasets, the outcome included in the imputation model, results combined with Rubin's rules.

3.8 Analytical methods (items 12a to 12g)

3.9 Class imbalance, fairness and model output (items 13 to 15)

• Class imbalance (13): if you rebalanced the data (over- or under-sampling), say how and how you recalibrated afterwards, because rebalancing distorts predicted risks. Example: no rebalancing.
• Fairness (14): describe the approaches used to assess and address fairness, and the groups compared. Example: performance by sex and by age group (under 65, 65 or over).
• Model output (15): say whether the model gives a probability, a score or a class, and the rationale for any risk threshold. Example: predicted probability; a 10% threshold agreed with clinicians.

3.10 Training versus evaluation and ethics (items 16 and 17)

State any differences between development and evaluation data in setting, eligibility, outcome and predictors. Name the ethics committee, the approval reference and the consent route (consent, waiver or exemption). See science/research-ethics-application for the application itself.

4. Open science

5. Patient and public involvement

[Describe patient and public involvement.] Example: a panel of six people with lived experience of kidney disease reviewed the outcome definition and the wording of the risk display, and co-authored the plain language summary.

6. Results

This section reports who was included, what the final model looks like and how well it performed.

6.1 Participants (items 20a to 20c)

Compare the evaluation data with the development data on key predictors and outcome rates (item 20c).

6.2 Model development and specification (items 21 and 22)

State the number of participants and events in each analysis and whether any participants were excluded because of missing data. Give the full model so that others can compute predictions: for a regression model the intercept and every coefficient; for other models the code, an application programming interface or a link to an implementation.

6.3 Model performance (items 23a and 23b)

Performance in key groups (item 23b and the fairness approach in 3.9):

Classification at the 10% threshold in the evaluation cohort, Example:

Sensitivity 75.1%, specificity 76.1%, positive predictive value 23.3%, negative predictive value 96.9%. Net benefit at the 10% threshold was 0.0419 (about 42 net true positives per 1,000 patients) against -0.0134 for treating everyone and 0 for treating no one. Show the decision curve over the clinically plausible range of thresholds, not one point only.

6.4 Model updating (item 24, E)

[Report the results of any recalibration or revision and the performance of the updated model.] Example: no updating was needed because calibration in the evaluation cohort was acceptable.

7. Discussion

The discussion interprets the findings, names limitations and explains the next steps toward use in care.

7.1 Interpretation (item 25)

[Overall interpretation, including fairness results, in the context of the objectives and previous studies.]

7.2 Limitations (item 26)

7.3 Usability in current care (items 27a to 27c)

If the model will be deployed as software that informs clinical decisions, check whether medical device regulation applies in your jurisdiction (for example the EU Medical Device Regulation 2017/745, UK medical device regulations, or US FDA software as a medical device policy), and see ai-data/model-monitoring-plan for monitoring drift after deployment.

8. TRIPOD+AI item map

9. Glossary and references

Related templates: science/observational-study, academic/stats-logistic-regression, academic/stats-analysis-plan, academic/stats-power-sample-size, science/preregistration, ai-data/model-card, ai-data/model-validation-report, science/target-trial-emulation (for causal questions, which prediction models do not answer).

Standard and licence note

Based on the TRIPOD+AI statement: Collins GS, Moons KGM, Dhiman P, Riley RD, et al., TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods, BMJ 2024;385:e078378, https://doi.org/10.1136/bmj-2023-078378, with the checklists at https://www.tripod-statement.org. Companion guidance named here: TRIPOD-LLM (Gallifant J, et al., Nature Medicine 2025;31:60-69) and PROBAST+AI (Moons KGM, et al., BMJ 2025;388:e082505). All were checked in October 2026; check the TRIPOD website for the current version. This template follows the item numbers and topics only, paraphrased in original text, and reproduces no checklist wording; complete the official checklist when submitting. The worked example is fictional. This is a reporting format, not clinical, statistical or regulatory advice. Template licence: MIT.