How to Choose Core Outcome Measurement Sets for Clinical Trials: OMERACT 11 Approves Filter 2.0
Bibliographic record
Abstract
OBJECTIVE: The Outcome Measures in Rheumatology (OMERACT) initiative works to develop core sets of outcome measures for trials and observational studies in rheumatology. At the OMERACT 11 meeting, substantial time was devoted to discussing a conceptual framework and a proposal for a more explicit working process to develop what we now propose to term core outcome measurement sets, collectively termed "OMERACT Filter 2.0." METHODS: Preconference work included a literature review, and discussion of preliminary proposals through face-to-face discussions and Internet-based surveys with people within and outside rheumatology. At the conference, 5 interactive sessions comprising plenary and small-group discussions reflected on the proposals from the viewpoint of previous and ongoing OMERACT work. These considerations were brought together in a final OMERACT presentation seeking consensus agreement for the Filter 2.0 framework. RESULTS: After debate, clarification, and agreed alterations, the final proposal suggested all core sets should contain at least 1 measurement instrument from 3 Core Areas: Death, Life Impact, and Pathophysiological Manifestations, and preferably 1 from the area Resource Use. The process of core set development for a health condition starts by selecting core domains within the areas ("core domain set"). This requires literature searches, involvement (especially of patients), and at least 1 consensus process. Next, developers select at least 1 applicable measurement instrument for each core domain. Applicability refers to the original OMERACT Filter and means that the instrument must be truthful (face, content, and construct validity), discriminative (between situations of interest) and feasible (understandable and with acceptable time and monetary costs). Depending on the quality of the instruments, participants formulate either a preliminary or a final "core outcome measurement set." At final vote, 96% of participants agreed "The proposed overall framework for Filter 2.0 is a suitable basis on which to elaborate a Filter 2.0 Handbook." CONCLUSION: Within OMERACT, Filter 2.0 has made established working processes more explicit and includes a broadly endorsed conceptual framework for core outcome measurement set development.
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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 | 0.790 | 0.839 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.033 | 0.024 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".