A web-based model to support patient-to-hospital allocation in mass casualty incidents
Bibliographic record
Abstract
BACKGROUND: In a mass casualty situation, evacuation of severely injured patients to the appropriate health care facility is of critical importance. The prehospital stage of a mass casualty incident (MCI) is typically chaotic, characterized by dynamic changes and severe time constraints. As a result, those involved in the prehospital evacuation process must be able to make crucial decisions in real time. This article presents a model intended to assist in the management of MCIs. The Mass Casualty Patient Allocation Model has been designed to facilitate effective evacuation by providing key information about nearby hospitals, including driving times and real-time bed capacity. These data will enable paramedics to make informed decisions in support of timely and appropriate patient allocation during MCIs. The model also enables simulation exercises for disaster preparedness and first response training. METHODS: Road network and hospital location data were used to precalculate road travel times from all locations in Metro Vancouver to all Level I to III trauma hospitals. Hospital capacity data were obtained from hospitals and were updated by tracking patient evacuation from the MCI locations. In combination, these data were used to construct a sophisticated web-based simulation model for use by emergency response personnel. RESULTS: The model provides information critical to the decision-making process within a matter of seconds. This includes driving times to the nearest hospitals, the trauma service level of each hospital, the location of hospitals in relation to the incident, and up-to-date hospital capacity. CONCLUSION: The dynamic and evolving nature of MCIs requires that decisions regarding prehospital management be made under extreme time pressure. This model provides tools for these decisions to be made in an informed fashion with continuously updated hospital capacity information. In addition, it permits complex MCI simulation for response and preparedness training.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".