Point of Care Ultrasound at a Remote Multi-Day Mass Gathering: A Prospective Case Series
Bibliographic record
Abstract
Study/Objective: To evaluate whether non-medical personnel feel adequately trained and prepared to act as first responders to potential medical emergencies at a multi-day music festival. Background: Music festivals are a high-risk environment for medical presentations. Although dedicated medical services are often present at such events, non-medical volunteers and staff generally outnumber those with formal medical roles and are more likely to make the first point of contact with attendees in distress. Preparation for foreseeable emergencies makes sound safety sense, and more recently litigation has also underscored its importance in minimizing liability. Using the chain of survival model, the provision of timely first responder care by appropriately trained personnel has the best chance of affecting outcomes by minimizing morbidity, mortality, liability and impact on local health care infrastructure. Methods: This study used an online survey provided to 2,200 non-medical staff and volunteers, at the 2016 edition of a weeklong electronic dance music event for 15,000 attendees. Results: A total of 369 personnel participated, of that 87% had direct contact with festival attendees and 85% had some form of formal first aid training. However, only 51% of this training was up to date, 19% had no CPR training at all, and 49% of those who had did not consider it up to date. A majority of respondents felt first aid training would benefit attendees, but that it should not be a requirement for their position. Respondents were receptive to basic and advanced training free of cost. Most felt comfortable acting as a first responder in scenarios dealing with unconscious, agitated, non-breathing or pulseless patients. Conclusion: Preparation of non-medical personnel for medical emergencies at music festivals can potentially increase safety and minimize negative outcomes. Such personnel appear comfortable with first response roles but may need help in maintaining training currency. Results may be applicable to other event types.
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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 | 0.001 |
| 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) | 0.000 | 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".