Mortality in Terrorist Attacks: A Unique Modal of Temporal Death Distribution
Bibliographic record
Abstract
BACKGROUND: Terror-related multiple casualty incidents (MCI) in Israel since September 2000 have resulted in a new pattern of injury as a result of the mechanisms of trauma. The objective of this study was to asses the temporal death distribution among the civilian casualties in the Jerusalem vicinity during a 3-year period. METHODS: All terrorist attacks in the Jerusalem district from September 2000 to September 2003 were included in this study. The data of all deaths were processed including the time of the attack, the evacuation time to the hospitals, and the time of death. RESULTS: During the study period 28 terror-related MCI occurred. A total of 2328 victims were injured and 273 died, for an overall fatality rate of 11.7%. A unique temporal death distribution was identified; 82.8% of the deaths occurred immediately, at the scene of the attack (scene death); of the remaining 17.2% of patients who died in the hospital, half died within 4 hours of arrival (immediate death), one quarter within 5-24 hours (early death), and one quarter later than that (late death). The temporal death distribution was significantly different when classifying the mechanism of trauma to suicide bombings versus shooting. The scene mortality was higher in the suicide bombings than in shooting attacks (86.7% versus 77%, P = 0.039 ). In contrast, the mortality within 1-24 hours was higher in the shooting attacks (17% versus 6.3%, P = 0.05). CONCLUSIONS: Terror-related MCI occurring in civilian settings have a unique temporal death distribution. A very high scene mortality is seen compared to the classical description of Donald Trunkey1 in 1983. The late deaths, which composed 30% of the mortality in civilian settings, comprise only 4.4% of the total mortality in MCIs. A rough estimate of the in-hospital mortality could be achieved after the first 4 hours, allowing the assessment and distribution of hospital resources. Futile care should be identified early and availability of ICU beds can be calculated according to the immediate mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".