Chemical, biological, radiological, nuclear, and explosives (CBRNEs) preparedness for sporting event mass gatherings: A systematic review of the literature
Bibliographic record
Abstract
OBJECTIVE: Sporting events often constitute mass gatherings (MGs) featuring large crowds of spectators and participants. Our objective is to understand the current state of emergency preparedness for sporting events by examining past MG sporting events to evaluate mitigation, preparedness, response, and recovery against chemical, biological, radiological, nuclear, and explosive (CBRNE) events. METHODS: In accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, a systematic literature review was carried out among 10 literature databases. The quality and risk of bias in each reviewed publication was assessed using the Mixed Methods Appraisal Tool. RESULTS: A total of 5,597 publications were identified. Of these, 81 papers were selected for full text reads and 25 publications were accepted. The included articles documented sporting events worldwide, ranging from incidents occurring from 1972 to 2020. Cross-cutting themes found in best practices and recommendations were strategic communication, surveillance, planning and preparedness, and training and response. CONCLUSION: More evidence-based guidelines are needed to ensure best practices in response and recovery for CBRNE incidents at sporting events. Public health risks as well as implementation barriers and opportunities to prepare for potential CBRNE threats at sporting event MGs require further investigation.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".