Clinical Approach to Inconclusive Subscapularis Tear Diagnosis: a Meta-analysis
Bibliographic record
Abstract
To identify factors associated with subscapularis (SSC) tears and provide a theoretical basis for clinical diagnosis, we included studies related to subscapularis tears published before February 1, 2023. We screened for six predictors across previous studies for the meta-analysis. The predictors included age, sex, coracoid overlap (CO), coracohumeral distance (CHD), impairment of the long head of the biceps tendon (LHB), and dominant arm. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the studies. The risk ratios (RRs) and the weighted mean differences (WMDs) were used to evaluate the effect size of categorical variables and continuous variables, respectively. The Egger test was used to assess the publication bias of the studies. Ten studies were included from seven countries. A total of 2 126 patients were enrolled, of whom 1 041 had subscapularis tears and 1 085 did not. The study showed that age (WMD, 4.23 [95% CI, 2.32-6.15]; P<.00001), coracoid overlap (WMD, 1.98 [95% CI, 1.55-2.41]; P<.00001), coracohumeral distance(WMD, -1.03 [95% CI, -1.17- -0.88]; P<.00001), and an injury of the long head of the biceps tendon (RR, 4.98 [95% CI, 3.75-6.61]; P<.00001) were risk factors for subscapularis tears. These risk factors can help clinicians identify subscapularis tears early and select appropriate interventions. The level of evidence is 3.
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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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.056 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".