Core Outcome Set–STAndards for Reporting: The COS-STAR Statement
Bibliographic record
Abstract
BACKGROUND: Core outcome sets (COS) can enhance the relevance of research by ensuring that outcomes of importance to health service users and other people making choices about health care in a particular topic area are measured routinely. Over 200 COS to date have been developed, but the clarity of these reports is suboptimal. COS studies will not achieve their goal if reports of COS are not complete and transparent. METHODS AND FINDINGS: In recognition of these issues, an international group that included experienced COS developers, methodologists, journal editors, potential users of COS (clinical trialists, systematic reviewers, and clinical guideline developers), and patient representatives developed the Core Outcome Set-STAndards for Reporting (COS-STAR) Statement as a reporting guideline for COS studies. The developmental process consisted of an initial reporting item generation stage and a two-round Delphi survey involving nearly 200 participants representing key stakeholder groups, followed by a consensus meeting. The COS-STAR Statement consists of a checklist of 18 items considered essential for transparent and complete reporting in all COS studies. The checklist items focus on the introduction, methods, results, and discussion section of a manuscript describing the development of a particular COS. A limitation of the COS-STAR Statement is that it was developed without representative views of low- and middle-income countries. COS have equal relevance to studies conducted in these areas, and, subsequently, this guideline may need to evolve over time to encompass any additional challenges from developing COS in these areas. CONCLUSIONS: With many ongoing COS studies underway, the COS-STAR Statement should be a helpful resource to improve the reporting of COS studies for the benefit of all COS users.
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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 | 0.685 | 0.844 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.012 | 0.018 |
| Research integrity | 0.025 | 0.032 |
| Insufficient payload (model declined to judge) | 0.012 | 0.021 |
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".