Presence of Additional Pathology in Low-Grade Acromioclavicular Joint Injuries
Bibliographic record
Abstract
OBJECTIVE: To determine if additional pathology is present in low-grade acromioclavicular (AC) joint injuries. DESIGN: Prospective case series. SETTING: Patients were assessed by primary care sports medicine physicians at a single institution between 2019 and 2023. PATIENTS: Patients aged 18 to 65 years diagnosed with a type I to III AC injury based on clinical and radiographic evaluation. INTERVENTION: Consenting patients underwent magnetic resonance imaging (MRI) evaluation within 21 days of injury. All injuries were treated nonoperatively. MAIN OUTCOME MEASURES: Additional pathologies identified on MRI were reported in a standardized fashion by fellowship-trained musculoskeletal radiologists. RESULTS: Twenty-nine patients (26 men/3 women) were consented with a mean (±SD) age of 28.6 ± 9.5 years. The mean time from injury to MRI was 8.1 ± 5.9 days. Twenty-three injuries were sport related, and 6 were accidental traumas. Based on MRI, injury type was reclassified in 16 of 29 patients, and 13 remained unchanged. Additional pathologies identified included 14 muscle injuries, 5 rotator cuff tears, 5 labral tears, 1 nondisplaced fracture, and 1 intra-articular body. CONCLUSION: MRI evidence suggests that most AC joint injuries are more severe than clinically diagnosed. Identifying additional pathology may alter diagnostic and treatment guidelines for type I to III AC joint injuries.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".