The OMERACT Core Domain Set for Clinical Trials of Shoulder Disorders
Bibliographic record
Abstract
OBJECTIVE: To reach consensus on the core domains to be included in a core domain set for clinical trials of shoulder disorders using the Outcome Measures in Rheumatology (OMERACT) Filter 2.1 Core Domain Set process. METHODS: At OMERACT 2018, the OMERACT Shoulder Working Group conducted a workshop that presented the OMERACT 2016 preliminary core domain set and its rationale based upon a systematic review of domains measured in shoulder trials and international Delphi sessions involving patients, clinicians, and researchers, as well as a new systematic review of qualitative studies on the experiences of people with shoulder disorders. After discussions in breakout groups, the OMERACT core domain set for clinical trials of shoulder disorders was presented for endorsement by OMERACT 2018 participants. RESULTS: The qualitative review (n = 8) identified all domains included in the preliminary core set. An additional domain, cognitive dysfunction, was also identified, but confidence that this represents a core domain was very low. The core domain set that was endorsed by the OMERACT participants, with 71% agreement, includes 4 "mandatory" trial domains: pain, function, patient global - shoulder, and adverse events including death; and 4 "important but optional" domains: participation (recreation/work), sleep, emotional well-being, and condition-specific pathophysiological manifestations. Cognitive dysfunction was voted out of the core domain set. CONCLUSION: OMERACT 2018 delegates endorsed a core domain set for clinical trials of shoulder disorders. The next step includes identification of a core outcome measurement set that passes the OMERACT 2.1 Filter for measuring each domain.
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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 | 0.524 | 0.596 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".