Resiliency of the Ontario health care system to care for casualties due to conflict with a near-peer adversary: A population-based modeling study
Bibliographic record
Abstract
BACKGROUND: The threat of conflict between near-peer adversaries provides unique challenges to any health care system. As evidenced by the Ukraine-Russia conflict, sustained combat across broad geographic areas has led to a large number of casualties. Furthermore, the utilization of thermobaric and incendiary weapons has been associated with high proportions of combined multisystem blunt, penetrating, thermal, and blast injuries. The management of large volume of casualties with complex injuries will require significant resources. Military and civilian health care systems that may face a sustained and large evacuation of casualties to the homeland for definitive and rehabilitative care must plan accordingly. METHODS: Actual health care resource utilization was calculated between January 1, 2017, and March 31, 2023, in Ontario, Canada, through analyses of population-based administrative data sets. Existing hospital resources were stratified into ward, intensive care (ICU), burn care, and rehabilitation and expressed as mean weekly bed days. We then modeled the weekly addition of evacuated casualties into the health care system with the outcome expressed as added capacity required assuming civilian standard of care would be maintained. Clinical scenarios, which vary in patient volume (84, 140, 280, and 560 new patients/week) and duration (4, 12, 26, and 52 weeks), were modeled. Models were limited to trauma centers or the entire health care system. RESULTS: Added trauma center ward and/or ICU capacity peak requirements ranged from +3% in short low-volume scenarios to +25% in long very high-volume scenarios (+1% to +9% respectively when the entire system was modeled). Rehabilitation capacity would require larger increases ranging from +4% to +37%. However, added burn capacity required ranged from +159% to +1,200% of existing capacity. DISCUSSION: Given that Ontario hospitals (ICU, burn, ward, and rehabilitation) typically run close to or exceed their funded capacity, additional casualty volumes will require new resources and innovative strategies to manage patient flow. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level III.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".