Psychometric properties of patient‐reported outcome questionnaires for patients with musculoskeletal disorders of the shoulder
Bibliographic record
Abstract
PURPOSE: To evaluate the psychometric properties of self-administered patient-reported outcome (PRO) questionnaires which were used in non-surgical homogeneous populations with musculoskeletal shoulder disorders. METHODS: The included studies were identified using eligibility criteria. The methodological quality of each article was assessed using the COSMIN checklist. The psychometric properties of original versions and translated versions of PROs were also assessed. RESULTS: Twenty articles were included. Two musculoskeletal shoulder disorders were identified that met the selection criteria: rotator cuff disease and glenohumeral instability. A total of 11 PROs were identified. In general, the methodological quality of the included studies is fair or poor. The Western Ontario Rotator Cuff Index (WORC) and the Shoulder Pain and Disability Index (SPADI) are the most frequently evaluated PROs for patients with rotator cuff disease, and their psychometric properties seem to vary according to what language that they are in. For glenohumeral instability, the Western Ontario Shoulder Instability Index (WOSI) and the Oxford Instability Shoulder Score (OISS) are the most frequently evaluated PROs, and their psychometric properties seem to be adequate. CONCLUSION: Using for rotator cuff disease is advised, for Norwegian users, the SPADI, WORC, Oxford Shoulder Score, and disabilities of the arm, shoulder and hand. Dutch and Persian users could use the WORC. For Greek speakers, the SPADI is recommended. Turkish users could use the rotator cuff quality-of-life measure. For glenohumeral instability, Dutch and Norwegian speakers could use the WOSI and the OISS. Italian, Japanese, and Turkish users could use the WOSI. For English users, the OISS and the Shoulder Rating Questionnaire are recommended. LEVEL OF EVIDENCE: III.
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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 | 0.051 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".