Associations among shoulder strength, glenohumeral joint motion, and clinical outcome after rotator cuff repair.
Bibliographic record
Abstract
Rotator cuff tears are a common condition causing pain and disability, but the relationships among clinical measures of shoulder function and measures of glenohumeral joint (GHJ) function are not well known. In the study reported here, dynamic in vivo GHJ motion was measured during abduction from biplane radiographs in 22 rotator cuff repair (RCR) patients and 36 control subjects. Isometric shoulder strength was measured and clinical outcomes were assessed using the Western Ontario Rotator Cuff (WORC) Index. Associations among WORC, GHJ motion, and several shoulder strength ratios were assessed with linear regression. An association was detected between higher ER/ABD (external rotation/coronal-plane abduction) strength ratio and a humerus positioned more inferiorly relative to the glenoid in control subjects and RCR patients. Higher ER/ABD strength ratio was also associated with better clinical outcome in RCR patients. These findings suggest a relationship between ER/ABD strength ratio and a more centrally located average superior/inferior contact center in RCR patients and control subjects. The ER/ABD strength ratio can be easily measured in a clinical setting and therefore can be used in larger studies to investigate its relation to clinical outcomes over time or perhaps to predict superior migration of the humeral head.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".