Epidemiology of Emergency Medical Search and Rescue in the North Shore Mountains of Vancouver, Canada, from 1995 to 2020
Bibliographic record
Abstract
INTRODUCTION: Little is known about the epidemiology of emergency medical search and rescue incidents globally. The purpose of this study was to describe the epidemiology of emergency medical search and rescue incidents in the North Shore Mountains of Vancouver, British Columbia, Canada. METHODS: This was a retrospective review and descriptive analysis of search and rescue incident reports created by North Shore Rescue over a 25 y period from 1995 to 2019, inclusive. Incident reports were screened for inclusion against a priori criteria defining a medical callout. The National Advisory Committee of Aeronautics (NACA) severity score was used as a method to grade medical acuity of included subjects. RESULTS: We included 906 subjects. Their median age was 35 y (interquartile range, 24-53), and 65% of subjects were men. Forty-one percent (n=371) of subjects were classified as non-trauma and 54% (n=489) as trauma. The top 3 activities were hiking (53%), biking (10%), and snow sports (10%). Forty-nine percent of incidents were classified as having a NACA score of ≥3. For subjects with trauma, the top 3 body regions were lower limb (52%), head (18%), and torso (12%). For subjects with non-traumatic conditions, the top 3 causes were mental health crises (25%), exposure (25%), and cardiovascular incidents (11%). CONCLUSIONS: Half of the incidents were serious enough to require medical assessment at a hospital (NACA score ≥3). Given this medical acuity, there is a need for evidence-based guidelines and core training competencies for mountain medical search and rescue. Standardized core data sets and outcomes are needed to monitor quality of care over time.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".