A Preliminary Core Domain Set for Clinical Trials of Shoulder Disorders: A Report from the OMERACT 2016 Shoulder Core Outcome Set Special Interest Group
Bibliographic record
Abstract
OBJECTIVE: The Outcome Measures in Rheumatology (OMERACT) Shoulder Core Outcome Set Special Interest Group (SIG) was established to develop a core outcome set (COS) for clinical trials of shoulder disorders. METHODS: In preparation for OMERACT 2016, we systematically examined all outcome domains and measurement instruments reported in 409 randomized trials of interventions for shoulder disorders published between 1954 and 2015. Informed by these data, we conducted an international Delphi consensus study including shoulder trial experts, clinicians, and patients to identify key domains that should be included in a shoulder disorder COS. Findings were discussed at a stakeholder premeeting of OMERACT. At OMERACT 2016, we sought consensus on a preliminary core domain set and input into next steps. RESULTS: There were 13 and 15 participants at the premeeting and the OMERACT 2016 SIG meeting, respectively (9 attended both meetings). Consensus was reached on a preliminary core domain set consisting of an inner core of 4 domains: pain, physical function/activity, global perceived effect, and adverse events including death. A middle core consisted of 3 domains: emotional well-being, sleep, and participation (recreation and work). An outer core of research required to inform the final COS was also formulated. CONCLUSION: Our next steps are to (1) analyze whether participation (recreation and work) should be in the inner core, (2) conduct a third Delphi round to finalize definitions and wording of domains and reach final endorsement for the domains, and (3) determine which instruments fulfill the OMERACT criteria 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 | 0.648 | 0.632 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".