Toward a Generalized Framework of Core Measurement Areas in Clinical Trials: A Position Paper for OMERACT 11
Bibliographic record
Abstract
OBJECTIVE: The Outcome Measures in Rheumatology (OMERACT) international consensus initiative has successfully developed core sets of outcome measures for trials of many rheumatologic conditions, but its expanding scope called for clarification and updating of its underlying conceptual framework and working process. To develop a core set of what we propose to call outcome measurement instruments, consensus must be reached both on what to measure and how to measure. This article deals with the first part: a framework necessary to ensure comprehensiveness of the domains chosen for measurement. We formulated a conceptual framework of core measurement areas in clinical trials, for discussion at the OMERACT 11 conference. METHODS: We formulated a framework and definitions of key concepts adapted from the literature, and followed an iterative consensus process (small group processes and an Internet-based survey) of those involved including patients, health professionals, and methodologists within and outside rheumatology. RESULTS: The draft framework comprises 4 core "areas": death, life impact (all aspects of how a patient feels or functions), resource use (monetary and other costs of the health condition and interventions), and pathophysiologic manifestations (disease-specific clinical and psychological signs, biomarkers, and potential surrogate outcome measures necessary to assess specific effects). The survey responses (262 of 2293, response rate 11%) indicated broad agreement with the draft framework and the proposed definitions of key concepts, including understandability and feasibility. A total of 283 comments were processed. CONCLUSION: In an iterative process, we have developed a generic framework for outcome measurement and working definitions of key concepts ready for discussion at the OMERACT 11 conference.
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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 | 0.765 | 0.632 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.031 | 0.034 |
| Open science | 0.016 | 0.023 |
| Research integrity | 0.024 | 0.046 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".