Preoperative Magnetic Resonance Imaging Accurately Detects the Arthroscopic Comma Sign in Subscapularis Tears
Bibliographic record
Abstract
PURPOSE: To assess the accuracy and reliability of routine preoperative magnetic resonance imaging (MRI) in the detection of the comma sign compared with the gold standard of arthroscopic findings. METHODS AND MATERIALS: Preoperative MRI exams in consecutive patients undergoing arthroscopic subscapularis tendon repair, over a 5-year time frame, were retrospectively reviewed for full-thickness tears of the subscapularis and supraspinatus tendons, fatty atrophy of the subscapularis and supraspinatus muscles, and status of the long head of the biceps tendon. Each case was also evaluated for presence or absence of a comma sign on MRI. Surgical findings served as the diagnostic standard of reference in determination of a comma sign. RESULTS: The study cohort included 45 male and 10 female patients (mean age, 56; range, 32-80 years). A comma sign was present at arthroscopy in 19 patients (34.5%). Interclass and intrarater correlation showed 100% agreement in preoperative assessment of a comma sign on MRI. MRI showed an overall accuracy of 83.6% in diagnosis of a comma sign (sensitivity, 63.2%; specificity, 94.4%; positive predictive value, 85.7%; negative predictive value, 82.9%; positive likelihood ratio, 11.37; negative likelihood ratio, 0.39). No statistically significant association was observed between an arthroscopic comma sign and patient demographics or MRI findings of full-thickness rotator cuff tears, muscle fatty atrophy, or long head of the biceps tendon pathology. CONCLUSIONS: MR imaging illustrates excellent reliability and good specificity and accuracy in detection of the arthroscopic comma sign in the setting of subscapularis tendon tearing. Detection of a comma sign on MRI may be important preoperative planning information in the arthroscopic management of patients with subscapularis tendon tears. LEVEL OF EVIDENCE: Level IV, retrospective diagnostic 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 | 0.001 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".