The Impact of Multiple Casualty Incidents on Clinical Outcomes
Bibliographic record
Abstract
BACKGROUND: Multiple casualty incidents (MCI) highlight discrepancies between patient needs and available resources. It is generally thought that heavy patient loads adversely affect trauma health care delivery. The purpose of this study was to identify the impact of multiple casualty situations on the clinical outcomes of injured patients. METHODS: All severely injured trauma patients (Injury Severity Score [ISS] > or = 12) who presented during a 12-month period to a regional trauma center were retrospectively reviewed. MCIs were defined as treating and admitting three or more trauma patients within a maximum of 3 hours. This cohort was compared with all other patients who did not meet MCI criteria. RESULTS: Ten percent (88/861) of all trauma patients were treated in an MCI setting. Groups did not vary among sex, age, ISS, or mechanism of injury (p > 0.05). MCI patients displayed a greater length of hospital stay, time to first surgical procedure, time to emergency laparotomy, and time spent in the emergency room (p < 0.05). MCI and non-MCI patients did not differ in ICU length of stay, postadmission morbidity, or mortality (p > 0.05). CONCLUSION: The impact of a MCI on the quality of trauma care has not been previously defined. MCI events delay definitive care and prolong a patient's length of stay. This is particularly concerning in the emergency department where a trauma center's ability to treat MCI patients effectively via an increased surge capacity relies on swift patient triage and flow. We are now investigating these issues in other trauma centers.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".