Who Revisits Medical Services at a Music Festival?
Bibliographic record
Abstract
Introduction: Attendees at music festivals rely upon on-site medical services for their emergency and medical care needs. Patients previously cared for can re-present for services at different times over the course of an event. Aim: To identify the proportion of visits that are repeat presentations at music festivals and discuss themes in the medical care needs of these potentially resource-intensive patients. Methods: This study included a review of prospectively enrolled patients presenting for health services over five years at a number of music festivals in Belgium and Canada. Patient data were extracted from existing databases of visits as well as visit documentation, and linked by name and date of birth to identify repeat visits. Data were de-identified and visit times, triage acuity, chief complaints, treatments, and discharge instructions were extracted. Results: Re-presentations constituted approximately 5% of all on-site medical visits. The majority were for minor care (e.g., wounds, dressings, foot care). Repeat visits for major issues included chronic disease (e.g., asthma, seizures, diabetes) and serial intoxications; these were high risk for transport to hospital. Festival duration was positively correlated with the number of patients with multiple visits. Three or more visits or visits in different years were rare occurrences. Discussion: At music festivals, a small but significant proportion of attendees utilize medical services repeatedly. Most are low acuity issues that could potentially be avoided with counseling or supplies at the initial visit. However, higher acuity re-registrations, both within and between event years, are a higher risk for transport and could benefit from early identification. Having a plan to identify and potentially remove the sicker, higher risk patients from the event could be important for safety and liability.
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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 | 0.001 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".