Preventable Deaths From Hemorrhage at a Level I Canadian Trauma Center
Bibliographic record
Abstract
BACKGROUND: Studies of trauma deaths have had a tremendous impact on the quality of contemporary trauma care. We studied causes of trauma death at a Level I Canadian trauma center, and tabulated preventable deaths from hemorrhage using explicit criteria. METHODS: Trauma registry data were used to identify all trauma deaths at our institution from January 1, 1999 to December 31, 2003. Demographics, mechanism, and time or location of death were recorded. Registry data analysis and selective chart or autopsy review were then performed to assign causes of death. RESULTS: A total of 558 consecutive trauma deaths were reviewed. Mean age was 48.7 (46.7-50.6), and mean Injury Severity Score was 38.8 (37.6-40.0); 29% were females. Blunt trauma represented 87% of all cases; penetrating injuries were only 13%. Central nervous system (CNS) injuries were the most frequent cause of death (60%), followed by hemorrhage (15%), and then combination CNS and hemorrhagic injuries (11%). Multiple organ failure caused 5% of deaths and 9% of deaths were from other causes. Of hemorrhagic deaths, 48% (n = 41) were from blunt injury, and 52% (n = 45) were from a penetrating mechanism. Of these hemorrhagic deaths, 16% were judged to be preventable because of significant delays in identifying the major source of hemorrhage. Hemorrhage from blunt pelvic injury was the major cause of exsanguination in 12 of 14 of these preventable deaths. CONCLUSIONS: Blunt injury is the major mechanism leading to trauma deaths. Massive bleeding from blunt pelvic injury is the major cause of preventable hemorrhagic deaths in our study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".