Severely Injured Geriatric Population: Morbidity, Mortality, and Risk Factors
Bibliographic record
Abstract
BACKGROUND: With an increasing life expectancy and more active elderly population, management of geriatric trauma patients continues to evolve. The aim was to describe the mechanism and injuries of severely injured geriatric patients and to identify risk factors associated with mortality. METHODS: The Trauma Registry at a Canadian Level I trauma center was queried for all trauma patients older than 65 years and injury severity score >15 from 2004 to 2006, resulting in a retrospective chart review of 276 patients. The data were subsequently analyzed using univariate and multivariate analysis. RESULTS: Average age was 81.5 years (mean injury severity score of 25). Most common comorbid illness was hypertension (57.3%) and most frequent mechanism of injury was falls (72.3%). The overall mortality was comparable with the US National Trauma Data Bank (26.8% vs. 32.0%, confidence interval, 0.00-0.10). Geriatric patients requiring intubation, blood transfusions, or suffering from head, C-spine, or chest trauma had an increased likelihood of death. In-hospital respiratory, gastrointestinal, or infectious complications also had higher likelihood of death. CONCLUSIONS: Falls continue to be the most frequent mechanism of injury in severely injured geriatric patients. Risk factors associated with a higher likelihood of death are identified. More research is needed to better understand this important and increasing group of trauma patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".