On the Way Out: An Analysis of Patient Transfers from Four Large-Scale North American Music Festivals Over Two Years
Bibliographic record
Abstract
INTRODUCTION: Music festivals are globally attended events that bring together performers and fans for a defined period of time. These festivals often have on-site medical care to help reduce the impact on local health care systems. Historically, the literature suggests that patient transfers off-site are frequently related to complications of substance use. However, there is a gap in understanding why patients are transferred to hospital when an on-site medical team, capable of providing first aid services blended with a higher level of care (HLC) team, is present. OBJECTIVE: The purpose of this study is to better understand patterns of injuries and illnesses that necessitate transfer when physician-led HLC teams are accessible on-site. METHODS: This is a prospective, descriptive case series analyzing patient encounter documentation from four large-scale, North American, multi-day music festivals. RESULTS/DISCUSSION: On-site medical teams that included HLC team members were present for the duration of each festival, so every team was able to "treat and release" when clinically appropriate. Over the course of the combined 34 event days, there were 10,406 patient encounters resulting in 156 individuals being transferred off-site for assessment, diagnostic testing, and/or treatment. A minority of patients seen were transferred off-site (1.5%). The patient presentation rate (PPR) was 16.5/1,000. The ambulance transfer rate (ATR) was 0.12/1,000 attendees, whereas the total transfer-to-hospital rate (TTHR), when factoring in non-ambulance transport, was 0.25/1,000. In contrast to existing literature on transfers from music festivals, the most common reason for transfer off-site was for musculo-skeletal (MSK) injuries (53.8%) that required imaging. CONCLUSION: The presence of on-site HLC teams impacted the case mix of patients transferred to hospital, and may reduce the number of transfers for intoxication. Confounding preconceptions, patients in the present study were transferred largely for injuries that required specialized imaging and testing that could not be performed in an out-of-hospital setting. These results suggest that a better understanding of the specific effects on-site HLC teams have on avoiding off-site transfers will aid in improving planning for music festivals. The findings also identify areas for further improvement in on-site care, such as integrated on-site radiology, which could potentially further reduce the impact of music festivals on local health services. The role of non-emergency transport vehicles (NETVs) deserves further attention.TurrisSA, CallaghanCW, RabbH, MunnMB, LundA. On the way out: an analysis of patient transfers from four large-scale North American music festivals over two yearsPrehosp Disaster Med. 2019;34(1):72-81.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".