Triage at Mass Gathering Events: Not an Emergency Department, and Not (Necessarily) a Disaster
Bibliographic record
Abstract
Introduction: Triage at mass gathering events (MGEs) has no standard protocol that is widely accepted and applied uniformly across event types and locations. This investigation describes the current state of published literature as it applies specifically to the triage of patient presentations at MGEs, and identifies key roles and important limitations of triage methods in use at events. Method: A literature review search strategy was employed (previously published, Turris et al, 2021) to search for event case reports published for the period from 2010-2022. Included papers were reviewed and data were extracted for all references to triage; authors were contacted for any missing details. Data extraction looked specifically for the following (if available) : triage mention, triage scale used, triage categories with patient counts, triage training and any information on clinical dispositions subsequent to triage assignment. Results: A total of 60 papers were included (Data extraction in progress, numbers to be finalized for presentation). Of these papers, a minority even made mention of triage, very few specified the triage scale used, and almost none described any triage training. Only a handful of case reports contained counts of patient presentation by triage categories. A couple of papers mentioned triage scales that were event type specific (sports, etc). Conclusion: Published literature to date contains limited details and agreement on triage methods in use at MGEs. Methods are largely from the emergency and disaster domains. Triage utility appears generally to be limited to designating location and provider, and for a snapshot of acuity post event. The use of triage scale has not been solely predictive of the need for transfer to hospital.
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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 | 0.042 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".