Shoulder Terrible Triad: Classification, Functional Results, and Prognostic Factors
Bibliographic record
Abstract
INTRODUCTION: The shoulder terrible triad (STT) is a traumatic anterior shoulder dislocation, associated with rotator cuff (RC) tear and nerve injury from the brachial plexus. This study aimed to describe the functional results and prognostic factors of surgery in patients with STT. METHODS: Thirty consecutive patients with acute STT were included at the same institution. All patients were examined with x-rays, MRI, and electromyography. Surgical treatment in the acute setting was indicated to address an RC injury or a displaced greater tuberosity fracture. Variables registered on the day of surgery were preoperative Constant and Western Ontario Rotator Cuff (WORC) scores and injury pattern. At final discharge, Constant, American Shoulder and Elbow Surgeons (ASES), WORC, and subjective shoulder value scores were recorded by an independent evaluator. RESULTS: Twenty-seven patients underwent a complete follow-up. The dominant arm was affected in 50% of cases. The mean follow-up was 27 (12 to 43) months. The mean WORC and Constant scores improved from 1,543 to 1,093 (P = 0.015) and 31 to 54 (P = 0.003), respectively. The ASES and subjective shoulder value scores at the end of the follow-up were 60 and 56 points, respectively. RC tears and nerve injuries that did not involve the axillary or suprascapular nerves were associated to better results than greater tuberosity fractures and injuries to the axillary or suprascapular nerves, respectively, in WORC (P = 0.028), Constant (P = 0.024), and ASES scores (P = 0.035). Preoperative WORC and Constant scores were independent prognostic factors. CONCLUSIONS: The most frequent patterns include complete RC tears, anterior capsular injuries, and an axillary nerve injury. Patients had improved functional scores at the end of follow-up after surgery. Better functional results were correlated to RC tears, injuries to nerves with innervation distal to the shoulder, and higher preoperative Constant and WORC scores.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".