Protocol for development of a checklist and guideline for transparent reporting of cluster analyses (TRoCA)
Bibliographic record
Abstract
Introduction Cluster analysis, a machine learning-based and data-driven technique for identifying groups in data, has demonstrated its potential in a wide range of contexts. However, critical appraisal and reproducibility are often limited by insufficient reporting, ultimately hampering the interpretation and trust of key stakeholders. The present paper describes the protocol that will guide the development of a reporting guideline and checklist for studies incorporating cluster analyses—Transparent Reporting of Cluster Analyses. Methods and analysis Following the recommended steps for developing reporting guidelines outlined by the Enhancing the QUAlity and Transparency Of health Research Network, the work will be divided into six stages. Stage 1: literature review to guide development of initial checklist. Stage 2: drafting of the initial checklist. Stage 3: internal revision of checklist. Stage 4: Delphi study in a global sample of researchers from varying fields ( n =≈) to derive consensus regarding items in the checklist and piloting of the checklist. Stage 5: consensus meeting to consolidate checklist. Stage 6: production of statement paper and explanation and elaboration paper. Stage 7: dissemination via journals, conferences, social media and a dedicated web platform. Ethics and dissemination Due to local regulations, the planned study is exempt from the requirement of ethical review. The findings will be disseminated through peer-reviewed publications. The checklist with explanations will also be made available freely on a dedicated web platform ( troca-statement.org ) and in a repository.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.288 | 0.563 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.122 | 0.038 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".