Incidence of Associated Injury in Posterior Shoulder Dislocation
Bibliographic record
Abstract
OBJECTIVE: Posterior shoulder dislocations are rare and often missed. Classically associated with seizures, very little is known about the incidence and type of associated injuries. Unfortunately, the majority of the literature consists of incidental reports or small case series. Our goal was to increase the strength of available data by performing a systematic review. DATA SOURCES: We searched EMBASE and PubMed for the terms "posterior shoulder dislocation." Our inclusion criteria were articles in either English or French with the words "posterior" and "dislocation" in the abstract or title. All reports of chronic cases or instability as well as those without patient information were excluded. Data regarding demographics, etiology, investigations, associated injuries, treatments, and outcomes were extracted. All data were analyzed by using SPSS 18.0 (IBM, Chicago, IL). RESULTS: A total of 766 articles were found of which 108 were retained for analysis. A total of 475 patients (543 shoulders) were compiled. Seizures were reported in 34% of cases. A majority of dislocations (65%) had associated injuries. Fracture was most common followed by reverse Hill-Sachs and cuff tears. In the absence of fracture or reverse Hill-Sachs injury, the risk of cuff tear increased nearly fivefold (odds ratio, 4.6; P = 0.016). CONCLUSION: Our results suggest the amount of associated injuries related to posterior shoulder dislocation is far greater than thought. We propose an investigation algorithm for acute posterior shoulder dislocations.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".