Assessment of function in patients with rotator cuff tears: Functional test versus self-reported questionnaire
Bibliographic record
Abstract
PURPOSE: The rotator cuff tears (RCT) are a well-known cause of shoulder pain and loss of upper extremity function. The purpose of this study was to evaluate the upper extremity function using two different methods in patients with RCT and to determine the parameters that influence the upper extremity function. MATERIALS AND METHODS: A sample of 38 patients (27-76 years; 10 men and 28 women) who were diagnosed with a chronic full-thickness RCT, confirmed by magnetic resonance imaging (MRI), was studied. Upper extremity function was determined using Western Ontario Rotator Cuff Index (WORC) and 9 Hole Peg Test (9PEG). Other assessments included active range of motion (ROM), muscle strength, shoulder pain, and scapular dyskinesis. RESULTS: There was a weak association between WORC scores and 9PEG. A statistically significant, negative relationship was found between 9PEG and ROM in supination, as well as muscle strength of shoulder extensors, adductors, internal and external rotators. CONCLUSIONS: In addition to the weak association between WORC and 9PEG, the difference between the parameters related to each method suggests that they should not be used interchangeably to determine the upper extremity function. We recommend the utilization of 9PEG instead of WORC in assessing the upper extremity function in the setting of loss of muscle strength. LEVEL OF EVIDENCE: Level IV, Therapeutic study.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".