Teres minor denervation and pathologies resulting in shoulder joint instability and rotator cuff tears: A retrospective cross-sectional MRI study
Bibliographic record
Abstract
Teres minor denervation (TMD) has gained increasing attention in recent years, particularly with the advent of magnetic resonance imaging (MRI). The potential association between TMD and shoulder instability or rotator cuff tear remains a subject of interest in the orthopedic community. In this retrospective and cross-sectional study, authors aim to investigate the potential association between TMD and shoulder instability or rotator cuff tears. Authors retrospectively analyzed MRI findings from 105 patients with TMD, focusing on rotator cuff pathologies, posterior labrocapsular complex (PLCC) tears, and posteroinferior glenohumeral joint capsule alterations. Authors assessed the association between TMD and rotator cuff and PLCC tears. For the multivariate analysis, partial proportional odds models were constructed for subscapularis (SSC) and SSP tears. Rotator cuff tears were present in 82.9% of subjects, with subscapularis (SSC) tears being the most frequent (77.1%). A significant association was observed between TMD and rotator cuff pathology (P = .002). PLCC tears were found in 82.3% of patients, and humeral position relative to the osseous glenoid was noted in 60% of patients with TMD. A significant association was identified between TMD and shoulder instability or labral/capsular abnormalities (P < .001). More than half of the cases exhibited a long tethering appearance toward the axillary neurovascular bundle on T1-weighted sagittal images. Our findings suggest that TMD is significantly associated with rotator cuff tears and shoulder instability. This study highlights the importance of identifying and treating PLCC tears in patients with TMD to address shoulder instability. Further research is needed to elucidate the role of TMD in the pathogenesis of shoulder instability and rotator cuff pathology.
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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 | 0.001 |
| 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 | 0.000 | 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".