Closed Reduction of Anterior Shoulder Dislocations Performed by Ski Patrollers in the Alpine Prehospital Environment: A Retrospective Review Demonstrating Efficacy in a Canadian Ski Resort
Bibliographic record
Abstract
INTRODUCTION: Shoulder dislocations are common ski hill injuries. Rapid reduction is known to improve outcomes; however, advanced providers are not always available to provide care to these patients. In 2017, nonmedical ski patrollers at Sunshine Village ski resort in Alberta, Canada, were trained to perform anterior shoulder dislocation (ASD) reductions. Program success was determined by a chart review after the 2020 ski season. METHODS: This study retrospectively reviewed data on patients who presented to Sunshine Village ski patrol with a suspected ASD and who met the study inclusion criteria from November 2017 through March 2020. Data were collected from ski patrol electronic patient care records regarding general demographics, reduction technique used, analgesia administration, and reduction success rates. RESULTS: Ninety-six cases were available for review after exclusions. Trained nonmedical ski patrollers successfully reduced 82 of these cases, resulting in an overall reduction success rate of 89%. Sixty-three (66%) of these patients had experienced first-time dislocations. Eighty-two (87%) patients were male, with a median age of 25 y. The most used technique was the Cunningham method (75%), and analgesia was administered to 70% of patients. CONCLUSIONS: This retrospective study documents the results of a quality assurance review of the treatment of ASD at Sunshine Village ski resort. With a success rate of 89%, the evidence supports the conclusion that nonmedical ski patrollers can successfully perform ASD reductions. We believe training ski patrollers to reduce ASD improved patient care in our austere environment by providing early definitive treatment with a high success rate.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".