Consensus Building in OMERACT: Recommendations for Use of the Delphi for Core Outcome Set Development
Bibliographic record
Abstract
OBJECTIVE: Developing international consensus on outcome measures for clinical trials is challenging. The following paper will review consensus building in Outcome Measures in Rheumatology (OMERACT), with a focus on the Delphi. METHODS: Based on the literature and feedback from delegates at OMERACT 2018, a set of recommendations is provided in the form of the OMERACT Delphi Consensus Checklist. RESULTS: The OMERACT delegates generally supported the use of the checklist as a guide. The checklist provides guidance for clearly outlining the multiple aspects of the Delphi process. CONCLUSION: OMERACT is deeply committed to consensus building and these recommendations should be considered a work in progress.
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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 | 0.704 | 0.753 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.010 | 0.025 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".