Two Worst Injuries in Different Body Regions Are Associated with Higher Mortality than Two Worst Injuries in the Same Body Region
Bibliographic record
Abstract
BACKGROUND: The Injury Severity Score (ISS) accounts for only one injury in each body region. The New Injury Severity Score (NISS) considers all injuries in a body region. Despite a big difference between the two scores in patients with multiple injuries, the NISS does not offer significant improvement in mortality prediction. This paper hypothesizes that two injuries in different body regions are associated with higher mortality than two injuries in the same body region, independently of injury severity. METHODS: The population consisted of 15,200 patients with two or more injuries from the Quebec Trauma Registry, Canada, abstracted between 1998 and 2004. The two worst injuries were considered. Logistic regression analysis was used to assess the mortality odds ratio of having two worst injuries in different body regions (DR) compared with two worst injuries in the same body region (SR), while adjusting for the severity and the body region of the two worst injuries and age. RESULTS: A total of 5,869 patients (49%) had their worst injuries in DR. DR patients had 75% higher risk of mortality than SR patients (odds ratio = 1.75, 95% confidence interval = 1.42-2.15). CONCLUSION: After accounting for differing injury severity, patients with their two worst injuries in different body regions have higher mortality than those with their two worst injuries in the same region. Results suggest that the observed effect is not due to a foible in the Abbreviated Injury Scale system but rather to physiologic, clinical, or organizational elements. The results of this study should be considered in the development of future injury severity instruments and may have implications for the care of patients with multiple injuries.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".