Assessment of Mortality in Older Trauma Patients Sustaining Injuries from Falls or Motor Vehicle Collisions Treated in Regional Level I Trauma Centers
Bibliographic record
Abstract
OBJECTIVE: To compare mortality in elderly trauma patients sustaining fall or motor vehicle collision (MVC) related injuries and who are subsequently treated at regional Level I (tertiary) trauma centers. SUMMARY BACKGROUND DATA: An increase in the mean age of the Canadian population is leading to a higher proportion of older patients injured in falls who are subsequently treated at Level 1 trauma centers in Quebec. The Level 1 centers were designed to treat younger patients injured in MVCs and violent acts. As a result, discordance may exist between the type of care supplied at these centers and the increased demand for care tailored to older trauma patients. METHODS: A retrospective cohort study comprised of 4,717 patients over the age of 65; 606 (12.8%) injured in MVCs and 4,111 (87.2%) in falls. The mean (SD) age was 79.6 (8.0) years and 67.9% were female. The mean (SD) Injury Severity Score (ISS) was 10.8 (7.4). Data were obtained from the Quebec Trauma Registry (QTR) for patients treated at 3 Level I trauma centers in the province of Quebec, Canada. The primary outcome measure in this study was mortality. RESULTS: Being injured in a fall was a strong predictor for mortality, with an odds ratio of 5.11 (95% C.I. = 1.84-14.17, P = 0.002). Additionally, the adjusted mortality rate was 25.3% among fall victims, versus 7.8% for MVC patients. Female gender, older age, higher ISS and an increasing number of injuries were all associated with heightened mortality. In contrast, the number of body regions injured, experiencing complications, sustaining a hip fracture, the Revised Trauma Score, the Prehospital Index and the Charlson (comorbidity) Index had no association with mortality in the Level I centers. CONCLUSIONS: Elderly patients sustaining fall-related injuries and treated at Level I trauma centers are at risk for excess mortality when compared with those injured in MVCs. Effective and efficient methods for treating this population must be determined.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".