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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".