Bibliographic record
Abstract
Triage is a complex process and is one means for determining which patients most need access to limited resources. Triage has been studied extensively, particularly in relation to triage in overcrowded emergency departments, where individuals presenting for treatment often are competing for the available stretchers. Research also has been done in relation to the use of prehospital and field triage during mass-casualty incidents and disasters. In contrast, scant research has been done to develop and test an effective triage approach for use in mass-gathering and mass-participation events, although there is a growing body of knowledge regarding the health needs of persons attending large events. Existing triage and acuity scoring systems are suboptimal for this unique population, as these events can involve high patient presentation rates (PPR) and, occasionally, critically ill patients. Mass-gathering events are dangerous; a higher incidence of injury occurs than would be expected from general population statistics. The need for an effective triage and acuity scoring system for use during mass gatherings is clear, as these events not only create multiple patient encounters, but also have the potential to become mass-casualty incidents. Furthermore, triage during a large-scale disaster or mass-casualty incident requires that multiple, local agencies work together, necessitating a common language for triage and acuity scoring. In reviewing existing literature with regard to triage systems that might be employed for this population, it is noted that existing systems are biased toward traumatic injuries, usually ignoring mitigating factors such as alcohol and drug use and environmental exposures. Moreover, there is a substantial amount of over-triage that occurs with existing prehospital triage systems, which may lead to misallocation of limited resources. This manuscript presents a review of the available literature and proposes a triage system for use during mass gatherings that also may be used in the setting of mass-casualty incidents or disaster responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".