Mass-gathering Medicine: Risks and Patient Presentations at a 2-Day Electronic Dance Music Event
Bibliographic record
Abstract
INTRODUCTION: Music festivals, including electronic dance music events (EDMEs), increasingly are common in Canada and internationally. Part of a US $4.5 billion industry annually, the target audience is youth and young adults aged 15-25 years. Little is known about the impact of these events on local emergency departments (EDs). METHODS: Drawing on prospective data over a 2-day EDME, the authors of this study employed mixed methods to describe the case mix and prospectively compared patient presentation rate (PPR) and ambulance transfer rate (ATR) between a first aid (FA) only and a higher level of care (HLC) model. RESULTS: There were 20,301 ticketed attendees. Seventy patient encounters were recorded over two days. The average age was 19.1 years. Roughly 69% were female (n=48/70). Forty-six percent of those seen in the main medical area were under the age of 19 years (n=32/70). The average length of stay in the main medical area was 70.8 minutes. The overall PPR was 4.09 per 1,000 attendees. The ATR with FA only would have been 1.98; ATR with HLC model was 0.52. The presence of an on-site HLC team had a significant positive effect on avoiding ambulance transfers. DISCUSSION: Twenty-nine ambulance transfers and ED visits were avoided by the presence of an on-site HLC medical team. Reduction of impact to the public health care system was substantial. CONCLUSIONS: Electronic dance music events have predictable risks and patient presentations, and appropriate on-site health care resources may reduce significantly the impact on the prehospital and emergency health resources in the host community.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".