Mortality at Music Festivals: Academic and Grey Literature for Case Finding
Bibliographic record
Abstract
OBJECTIVE: Deaths at music festivals are not infrequently reported in the media; however, the true mortality burden is difficult to determine as the deaths are not yet systematically documented in the academic literature. METHODS: This was a literature search for case examples using academic and gray literature sources, employing both retrospective and prospective searches of media sources from 1999-2014. RESULTS: The gray literature documents a total of 722 deaths, including traumatic (594/722; 82%) and non-traumatic (128/722; 18%) causes. Fatalities were caused by trampling (n=479), motor-vehicle-related (n=39), structural collapses (n=28), acts of terror (n=26), drowning (n=8), assaults (n=6), falls (n=5), hanging (n=2), and thermal injury (n=2). Non-traumatic deaths included overdoses (n=96/722; 13%), environmental causes (n=8/722; 1%), natural causes (n=10/722; 1%), and unknown/not reported (n=14/722; 2%). The majority of non-trauma-related deaths were related to overdose (75%). The academic literature documents trauma-related deaths (n=368) and overdose-related deaths (n=12). One hundred percent of the trauma-related deaths reported in the academic literature also were reported in the gray literature (n=368). Mortality rates cannot be reported as the total attendance at events is not known. CONCLUSIONS: The methodology presented in this manuscript confirms that deaths occur not uncommonly at music festivals, and it represents a starting point in the documentation and surveillance of mortality. Turris SA , Lund A . Mortality at music festivals: academic and grey literature for case finding. Prehosp Disaster Med. 2017;32(1):58-63.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".