Acromioclavicular joint dislocation and concomitant labral lesions: a systematic review
Bibliographic record
Abstract
Acromioclavicular (AC) joint dislocations frequently co-occur with intra-articular glenohumeral pathologies. Few comprehensive studies have focused on labral tears specifically associated with AC joint trauma. This systematic review will address this gap. A comprehensive electronic search was conducted across PubMed, Cochrane Library, and Google Scholar (pages 1-20) spanning from 1976 to May 19, 2023. Seven studies met the inclusion criteria for this systematic review, consisting of three retrospective studies and four case series. These studies collectively involved 1,044 patients, of whom 282 had concomitant labral lesions. The pooled prevalence of intra-articular labral injuries associated with acute AC joint dislocation was 27%. The prevalence of these labral lesions varied significantly between studies, ranging from 13.9% to 84.0% of patients, depending on the study and the grade of AC joint dislocation. Various types of labral tears were reported, with superior labrum anterior to posterior (SLAP) lesions being the most common. The prevalence of SLAP lesions ranged from 7.2% to 77.4%, with higher grades of AC joint dislocations often associated with a higher prevalence of SLAP tears. Moreover, grade V dislocations exhibited a complete correlation with SLAP tears. The studies yielded contradictory findings regarding older age and higher grades of AC joint dislocation as risk factors for concurrent labral lesions. This review underscores the frequent association between labral lesions and AC joint dislocations, particularly in cases of lower-grade injuries. Notably, SLAP lesions emerged as the predominant type of labral tear.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".