Characteristics of adult patients transferred by emergency medical services from a large-scale music festival to emergency departments in Calgary, Alberta
Bibliographic record
Abstract
Introduction Mass Gathering Events (MGEs) are characterized by large crowds and pose unique prehospital challenges. High attendance creates medical demands that exceed local norms, requiring dedicated onsite medical services. Many factors influence Patient Presentation Rates (PPRs) and Transfer to Hospital Rates (TTHRs) making them difficult to predict for events. One annual Electronic Dance Music Festival (EDMF) in Calgary attracts about 35,000 attendees engaging in activities like dancing and recreational drug use. Current literature lacks clear criteria for transferring patients from MGEs to Emergency Departments (EDs) via local Emergency Medical Services (EMS). Study Objective This study aimed to quantify local EMS use for transferring adult patients from this EDMF to Calgary acute care centres between 2015–2019. Methods A retrospective chart review was conducted for patients aged 18+ who presented to adult EDs or urgent care centres (UCCs) in Calgary during the two-day EDMF. Results Most patients transported by EMS were young males. Older attendees more often sought care outside the event by walk-in or self-initiated ambulance transport to ED. Toxic effects of illicit substances were the leading cause for seeking medical help. Conclusions Intoxication and poisonings accounted for 63.9% of MGE-related presentations at Calgary acute care centres. Enhanced onsite medical care lowered Ambulance Transfer Rates (ATR) but did not affect overall TTHRs. These results can help develop evidence-based guidelines to assist host communities in planning medical resources, reducing healthcare burdens from similar MGEs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".