Clinical Factors Predicting Fractures Associated with an Anterior Shoulder Dislocation
Bibliographic record
Abstract
OBJECTIVES: To identify risk factors for fractures associated with an anterior shoulder dislocation treated in an emergency department (ED). METHODS: A retrospective case-control study over five years of patients with an anterior shoulder dislocation was accomplished in a university-affiliated ED. Chart review identified possible predictors of fractures. Comparing the profile of patients having a clinically important fracture associated with their shoulder dislocation (cases) with those sustaining a noncomplicated dislocation (controls) provided the outcome measure. RESULTS: A total of 334 patients were included in the study. Eighty-five (25.5%) had a clinically important fracture-dislocation, and the remaining 249 (74.5%) sustained a noncomplicated shoulder dislocation. Chi-square, logistic regression, and recursive partitioning analysis showed three significant factors for the presence of fracture-dislocation: 1) age 40 years or older, 2) a first episode of dislocation, and 3) mechanism of injury (i.e., a fall greater than one flight of stairs, a fight/assault episode, or a motor vehicle crash). A multiple logistic regression model estimated the significant adjusted odds ratios (and their 95% confidence intervals [95% CIs]) for each of the three factors: 5.18 (95% CI = 2.74 to 9.78), 4.23 (95% CI = 1.82 to 9.87), and 4.06 (95% CI = 1.95 to 8.48), respectively. A predictive model using any one of the three factors reached a sensitivity of 97.7% (95% CI = 91.8% to 99.4%), a specificity of 22.9% (95% CI = 18.1% to 28.5%), and a negative predictive value of 96.6% (95% CI = 88.3% to 99.6%). CONCLUSIONS: Three risk factors predict clinically important fractures that are associated with shoulder dislocation: age, first episode, and mechanism of dislocation. A prospective validation may lead to standardized use of prereduction radiographs of the shoulder in the ED.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".