Updating the OMERACT Filter: Core Areas as a Basis for Defining Core Outcome Sets
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: The Outcome Measures in Rheumatology (OMERACT) Filter provides guidelines for the development and validation of outcome measures for use in clinical research. The "Truth" section of the OMERACT Filter presupposes an explicit framework for identifying the relevant core outcomes that are universal to all studies of the effects of intervention effects. There is no published outline for instrument choice or development that is aimed at measuring outcome, was derived from broad consensus over its underlying philosophy, or includes a structured and documented critique. Therefore, a new proposal for defining core areas of measurement ("Filter 2.0 Core Areas of Measurement") was presented at OMERACT 11 to explore areas of consensus and to consider whether already endorsed core outcome sets fit into this newly proposed framework. METHODS: Discussion groups critically reviewed the extent to which case studies of current OMERACT Working Groups complied with or negated the proposed framework, whether these observations had a more general application, and what issues remained to be resolved. RESULTS: Although there was broad acceptance of the framework in general, several important areas of construction, presentation, and clarity of the framework were questioned. The discussion groups and subsequent feedback highlighted 20 such issues. CONCLUSION: These issues will require resolution to reach consensus on accepting the proposed Filter 2.0 framework of Core Areas as the basis for the selection of Core Outcome Domains and hence appropriate Core Outcome Sets for clinical trials.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it