What Do the OMERACT Shoulder Core Set Candidate Instruments Measure? An Analysis Using the Refined International Classification of Functioning, Disability, and Health Linking Rules
Bibliographic record
Abstract
OBJECTIVE: The objective of this paper is to assess the content and measurement constructs of the candidate instruments for the domains of "pain" and "physical function/activity" in the Outcome Measures in Rheumatology (OMERACT) shoulder core set. The results of this International Classification of Functioning, Disability, and Health (ICF)-based analysis may inform further decisions on which instruments should ultimately be included in the core set. METHODS: The materials for the analysis were the 13 candidate measurement instruments within pain and physical function/activity in the shoulder core domain set, which either passed or received amber ratings (meaning there were some issues with the instrument) in the OMERACT filtering process. The content of the candidate instruments was extracted and linked to the ICF using the refined linking rules. The linking rules enhance the comparability of instruments by providing a comprehensive overview of the content of the instruments, the context in which the measurements take place, the perspectives adopted, and the types of response options. RESULTS: The ICF content analysis showed a large variation in content and measurement constructs in the candidate instruments for the shoulder core outcome measurement set. CONCLUSION: Two of 6 pain instruments include constructs other than pain. Within the physical function/activity domain, 2 candidate instruments matched the domain, 3 included additional content, and 2 included meaningful concepts in the response options, suggesting that they should be omitted as candidate instruments. The analyses show that the content in most existing instruments of shoulder pain and functioning extends across core set domains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".