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tripod-check

📁 htlin222/dotfiles 📅 4 days ago
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5
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#34737
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安装命令
npx skills add https://github.com/htlin222/dotfiles --skill tripod-check

Agent 安装分布

claude-code 5
mcpjam 4
kilo 4
junie 4
windsurf 4
zencoder 4

Skill 文档

TRIPOD+AI Compliance Checker

Audit prediction model and clinical AI manuscripts against the TRIPOD+AI (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis + AI extension) 27-item checklist.

Workflow

  1. Read the full manuscript
  2. Identify study phase: Development (D), Evaluation (E), or Both (D;E)
  3. Identify modelling approach: regression, machine learning, deep learning, ensemble
  4. Walk through each item; note applicability column (D, E, or D;E)
  5. For each applicable item, assign: Reported / Partial / Missing / N/A
  6. Quote the relevant manuscript text as evidence
  7. Output a compliance summary + actionable fixes

TRIPOD+AI Checklist (27 Items)

Title and Abstract

# Applies Topic Requirement
1 D;E Title Identify as developing/evaluating a prediction model; specify target population, outcome, and modelling approach (regression vs ML)
2 D;E Abstract Structured summary following TRIPOD+AI for Abstracts

Introduction

# Applies Topic Requirement
3a D;E Healthcare context Explain diagnostic/prognostic setting; rationale for model; reference existing models
3b D;E Target population Describe intended population, where in care pathway, who are intended users
3c D;E Health inequalities Describe known health inequalities across demographic/socioeconomic groups; address fairness
4 D;E Objectives State objectives; specify whether development, evaluation, or both

Methods — Data and Participants

# Applies Topic Requirement
5a D;E Data sources Describe source(s) of data; justify selection; assess representativeness
5b D;E Data dates Start/end dates of participant accrual; end of follow-up for prognostic models
6a D;E Setting Study setting (primary/secondary care, general population); number and location of centres
6b D;E Eligibility Inclusion and exclusion criteria
6c D;E Treatments Treatments received; how handled during development/evaluation

Methods — Data Preparation and Outcome

# Applies Topic Requirement
7 D;E Data preparation All preprocessing, cleaning, harmonisation steps; quality checks; consistency across demographic groups
8a D;E Outcome definition Define predicted outcome; time horizon for prognostic models; assessment methods; consistency across subgroups
8b D;E Outcome assessors For subjective outcomes: assessor qualifications and demographics
8c D;E Outcome blinding Whether outcome assessment was blinded to predictor information

Methods — Predictors

# Applies Topic Requirement
9a D Predictor selection Describe and justify initial predictor choice and pre-selection
9b D;E Predictor definition Define all predictors; how and when measured; blinding procedures
9c D;E Predictor assessors For subjective predictors: assessor credentials and demographics

Methods — Sample Size and Missing Data

# Applies Topic Requirement
10 D;E Sample size How determined; justify sufficiency; include calculation details
11 D;E Missing data Approach to missing data with justification

Methods — Analytical Approaches

# Applies Topic Requirement
12a D Data partitioning How data allocated to development/evaluation; partitioning strategy
12b D Predictor handling How predictors handled (functional forms, transformations, standardisation)
12c D Model building Model type with rationale. For ML: architecture, hyperparameter tuning, training procedures. Internal validation method
12d D;E Heterogeneity How variability across clusters (hospitals, countries) was handled
12e D;E Performance evaluation Discrimination (c-statistic/AUC), calibration methods, clinical utility; model comparison if applicable
12f E Model updating Recalibration or updating approaches
12g E Prediction calculation How predictions generated; formula, code, or API details

Methods — Class Imbalance and Fairness

# Applies Topic Requirement
13 D;E Class imbalance Whether imbalance methods used, why, implementation, recalibration steps
14 D;E Fairness assessment Approaches to assess and address fairness across demographic groups

Methods — Model Specifications and Ethics

# Applies Topic Requirement
15 D Model output Output type (probabilities vs classifications); classification thresholds and rationale
16 D;E Dev vs eval differences Differences between development and evaluation in settings, eligibility, outcome, predictors
17 D;E Ethical approval IRB/ethics committee; consent procedures or waiver

Open Science

# Applies Topic Requirement
18a D;E Funding Funding sources and funder role
18b D;E Conflicts All author disclosures
18c D;E Protocol Where protocol accessible; or state not prepared
18d D;E Registration Registry name and number; or state not registered
18e D;E Data sharing Data availability; access restrictions and terms
18f D;E Code sharing Analytical code availability; access conditions

Patient and Public Involvement

# Applies Topic Requirement
19 D;E PPI Patient/public involvement in design, conduct, reporting; or state none

Results

# Applies Topic Requirement
20a D;E Participant flow Flow of participants; outcome event counts; follow-up time; flow diagram recommended
20b D;E Participant characteristics Demographics and key characteristics overall and per setting; predictor values, treatments, sample size, events, missing data; differences across demographic groups
20c E Data comparison Compare predictor distributions between evaluation and development datasets
21 D;E Participant counts Participants and events for each analysis phase (development, tuning, evaluation)
22 D Full model specification Complete model details for reproduction: regression coefficients/intercept, or model code/object/API
23a D;E Performance Performance measures with CIs; subgroup results; calibration plots
23b D;E Heterogeneity results Performance variation across clusters
24 E Model updating results Updated model and its performance

Discussion

# Applies Topic Requirement
25 D;E Interpretation Overall interpretation; fairness considerations; comparison to existing models
26 D;E Limitations Non-representativeness, sample size, overfitting, missing data, measurement bias, generalisability
27a D Poor quality input How model handles poor quality, missing, or out-of-range input data at deployment
27b D User requirements Level of user interaction needed; expertise required
27c D;E Future research Next steps: external validation, implementation, generalisability studies

ML/AI-Specific Emphasis

These items have expanded requirements for ML/AI models compared to traditional regression:

Item ML/AI Extra Requirements
7 (Data preparation) Feature engineering, data augmentation, normalisation pipelines
12c (Model building) Full architecture spec, hyperparameter search space, training/validation split, early stopping, regularisation
13 (Class imbalance) SMOTE, oversampling, undersampling, cost-sensitive learning
14 (Fairness) Algorithmic fairness metrics across demographic groups (new in TRIPOD+AI)
3c (Health inequalities) Equity considerations for model deployment (new in TRIPOD+AI)
18e-f (Open science) Model weights, training code, inference API sharing
22 (Model specification) Model weights/code/API, not just coefficients

Common TRIPOD+AI Gaps

Frequently Missing Fix
Item 3c (Health inequalities) Add paragraph on known demographic disparities in the prediction problem
Item 12c (Full ML pipeline) Document architecture, hyperparameters, training procedure, validation strategy
Item 14 (Fairness) Report model performance stratified by sex, age, race/ethnicity
Item 22 (Model specification) Share model code/weights via GitHub or provide formula with all coefficients
Item 18e-f (Data/code sharing) Publish code on GitHub; share de-identified data or explain restrictions
Item 19 (PPI) State whether patients/public were involved; if not, say so explicitly
Item 10 (Sample size) Use Riley et al. criteria for prediction model sample size

Output Format

TRIPOD+AI Compliance Report
Study phase: [Development / Evaluation / Both]
Modelling approach: [Regression / ML / Deep Learning / Ensemble]
Manuscript: [filename]

Summary: X/27 Reported | Y Partial | Z Missing | W N/A
(Items assessed based on study phase: D-only / E-only / D;E)

ML/AI-SPECIFIC GAPS:
  [Item #] [Topic] — [What's needed for ML/AI compliance]

OTHER MISSING:
  [Item #] [Topic] — [What's needed]

PARTIAL ITEMS:
  [Item #] [Topic] — [What's present] → [What's missing]

Open science:
  Code sharing: [Available (URL) / Not available / Not stated]
  Data sharing: [Available (URL) / Not available / Not stated]
  Registration: [Registered (ID) / Not registered / Not stated]

Extensions

  • TRIPOD-LLM (2024, Nature Medicine): Extension for studies using large language models in biomedical/healthcare. Adds 19 items covering explainability, transparency, human oversight, and task-specific LLM considerations.
  • PROBAST (Prediction model Risk Of Bias ASsessment Tool): Companion tool for assessing risk of bias; use alongside TRIPOD+AI for quality appraisal.

Related Skills

  • /manuscript — Overall manuscript writing and anti-pattern scanning
  • /strobe-check — If the prediction model is developed from an observational cohort, also run STROBE
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