Developing Core Outcome Measurement Sets for Clinical Trials: OMERACT Filter 2.0
Bibliographic record
Abstract
BACKGROUND: Lack of standardization of outcome measures limits the usefulness of clinical trial evidence to inform health care decisions. This can be addressed by agreeing on a minimum core set of outcome measures per health condition, containing measures relevant to patients and decision makers. Since 1992, the Outcome Measures in Rheumatology (OMERACT) consensus initiative has successfully developed core sets for many rheumatologic conditions, actively involving patients since 2002. Its expanding scope required an explicit formulation of its underlying conceptual framework and process. METHODS: Literature searches and iterative consensus process (surveys and group meetings) of stakeholders including patients, health professionals, and methodologists within and outside rheumatology. RESULTS: To comprehensively sample patient-centered and intervention-specific outcomes, a framework emerged that comprises three core "Areas," namely Death, Life Impact, and Pathophysiological Manifestations; and one strongly recommended Resource Use. Through literature review and consensus process, core set development for any specific health condition starts by identifying at least one core "Domain" within each of the Areas to formulate the "Core Domain Set." Next, at least one applicable measurement instrument for each core Domain is identified to formulate a "Core Outcome Measurement Set." Each instrument must prove to be truthful (valid), discriminative, and feasible. In 2012, 96% of the voting participants (n=125) at the OMERACT 11 consensus conference endorsed this model and process. CONCLUSION: The OMERACT Filter 2.0 explicitly describes a comprehensive conceptual framework and a recommended process to develop core outcome measurement sets for rheumatology likely to be useful as a template in other areas of health care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.510 | 0.683 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".