Can mesomorphic somatotype be a predisposing factor for rotator cuff tears?
Bibliographic record
Abstract
The aim of our study was to evaluate the anthropometric features used in the determination of somatotypes of patients with rotator cuff tears (RCT) and the condition before and after physiotherapy. In our study, 84 patients (43 female, 41 male) who were admitted to Tekirdağ State Hospital in 2020-2021 and diagnosed with RCT were included. Participants were grouped and evaluated in terms of age and gender. Visual analog scale (VAS) scoring for pain, Western Ontario Rotator Cuff (WORC) index, Shoulder Pain and Disability Index (SPADI), and Quick Disabilities of Arm, Shoulder, and Hand (DASH) scores for shoulder function and range of motion (ROM) values were measured. The Heath-Carter method was used for somatotype determination. Conventional physical therapy was applied to patients. 64 (76.2%) patients had endomorph-mesomorph, 14 (17%) had mesomorph-endomorph, 4 (5%) had mesomorph-endomorph, 1 (1%) had balanced mesomorph and 1 (1%) had ectomorph-endomorph somatotype. 71.4% of the participants younger than 45 years of age and 50% of those between 45 and 55 had partial tears; 80% of those between 56 and 65 and 76.5% of those older than 65 had a complete tear. Of the patients, 53.3% had cardiovascular diseases, 41.3% had problems with the digestive system, 25% had diabetes, and 24% had problems with the musculoskeletal system. The presence of RCT in the non-dominant shoulder was detected in 33.5% of the patients. There were statistically significant differences between the median values of VAS, Quick DASH, WORC, SPADI, and ROM before and after physical therapy. Regardless of gender, RCT patients had predominantly mesomorphic body types. Individuals with mesomorphic body types are more likely to have RCT. The presence of full-thickness tears was found to be closely related to advanced age. Physiotherapy has been proven to be beneficial for RCT patients in our 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".