Bibliographic record
Abstract
Introduction: Electronic Dance Music events (EDMs) are complex mass gatherings and given published rates of illnesses, injuries, and hospitalizations, these events can place an additional burden on local health care services. Accordingly, during the planning process for EDMs many stakeholders are involved; however, local hospitals, a key part of the medical safety plan, are often excluded. In this case report, it is posited that the involvement of local hospital(s) and the resulting integration of on-site and acute-care service provision during an event, ultimately reduces the burden placed on local hospitals. Methods: Case report; synthesis of published literature. Results: A 25,000 person per day, two-day mass gathering EDM event trialed a model of collaborative planning with a local community hospital. Planning included the identification of a hospital liaison, pre-event teleconferences between event staff, contracted and public medical response teams, emergency management teams, harm reduction practitioners, public health, and hospital personnel. Throughout the collaborative planning process, vital information was shared in order to optimize patient continuity of care and streamline the transition of care from site medical response to an acute care setting. Outcomes included the prevention of unnecessary transfers to the hospital; however, those patients who required transfer had their initial treatment started prior to leaving the venue. Further, collaborative planning also contributed to improved bidirectional data sharing to better understand the impact on the local hospital of the event, including transfers from the onsite medical team as well as transports from the community and self-presentations for care. Discussion: The collaboration of onsite medical and hospital teams improved the delivery of essential medical care to the patrons of the event and added a layer to the safety planning process essential to mass gathering events.
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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 | 0.030 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".