Delays to initial reduction attempt are associated with higher failure rates in anterior shoulder dislocation: a retrospective analysis of factors affecting reduction failure
Bibliographic record
Abstract
INTRODUCTION: Little is understood about the relationship between delay to treatment and initial reduction success for anterior shoulder dislocation. Our study examines whether delays to initial treatment, from injury and hospital presentation, are associated with higher reduction failure rates for anterior shoulder dislocation. METHODS: A retrospective database and chart review was performed for patients undergoing intravenous sedation for attempted reduction of anterior shoulder dislocation in the emergency department (ED). Stepwise regression analysis was performed to identify predictors of reduction failure. Key variables analysed were the duration of the wait in the ED, the interval between the time of injury and first intervention and the interval from time of injury to arrival at the ED. Possible confounding variables analysed included age, gender, dose of sedative agent, qualifications of the reducing physician and whether the dislocated shoulder was recurrent. RESULTS: The duration of the intervals from injury to first reduction attempt and from arrival at the ED to first reduction attempt were both independent predictors of a higher reduction failure rate (OR=1.07, 95% CI 1.02 to 1.13; OR=1.19, 95% CI 1.05 to 1.34). Every interval of 10 min increased the odds of a failed reduction attempt by 7% and 19%, respectively. Overall, shoulder reduction was successful during the initial sedation event in 97 cases (92%) and unsuccessful in nine cases (8%). CONCLUSIONS: Delays to first reduction attempt either from the time of injury or within the ED are associated with a lower reduction success rate for anterior 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".