An Analysis of Mass Casualty Incidents in the Setting of Mass Gatherings and Special Events
Bibliographic record
Abstract
Abstract Objectives Mass gatherings (MGs) and special events typically involve large numbers of people in unfamiliar settings, potentially creating unpredictable situations. To assess the information available to guide emergency services and onsite medical teams in planning and preparing for potential mass casualty incidents (MCIs), we analyzed the literature for the past 30 years. Methods A search of the literature for MCIs at MGs from 1982 to 2012 was conducted and analyzed. Results Of the 290 MCIs included in this study, the most frequently reported mechanism of injury involved the movement of people under crowded conditions (162; 55.9%), followed by special hazards (eg, airplane crashes, pyrotechnic displays, car crashes, boat collisions: 57; 19.6%), structural failures (eg, building code violations, balcony collapses: 38; 13.1%), deliberate events (26; 9%), and toxic exposures (7; 2.4%). Incidents occurred in Asia (71; 24%), Europe (69; 24%), Africa (48; 17%), North America (48; 27%), South America (27; 9%), the Middle East (25; 9%), and Australasia (2; 1%). A minimum of 12 877 deaths and 27 184 injuries resulted. Conclusions Based on our findings, we recommend that a centralized database be created. With this database, researchers can further develop evidence to guide prevention efforts and mitigate the effects of MCIs during MGs. ( Disaster Med Public Health Preparedness . 2014;0:1-7)
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".