Epidemiology, injury pattern and outcome of older trauma patients: A 15-year study of level-I trauma centers
Bibliographic record
Abstract
BACKGROUND: Older adults have become a significant portion of the trauma population. Exploring their specificities is crucial to better meet their specific needs. The primary objective was to evaluate the temporal changes in the incidence, demographic and trauma characteristics, injury pattern, in-hospital admission, complications, and outcome of older trauma patients. METHODS: A multicenter retrospective cohort study was conducted using the Quebec Trauma Registry. Patients aged ≥16 years admitted to one of the three adult level-I trauma centers between 2003 and 2017 were included. Descriptive analyses and trend-tests were performed to describe temporal changes. RESULTS: A total of 53,324 patients were included, and 24,822 were aged ≥65 years. The median [IQR] age increased from 57[36-77] to 67[46-82] years, and the proportion of older adults rose from 41.8% in 2003 to 54.1% in 2017. Among those, falls remain the main mechanism (84.7%-88.3%), and the proportion of severe thorax (+8.9%), head (+8.7%), and spine (+5%) injuries significantly increased over time. The proportion of severely injured older patients almost doubled (17.6%-32.3%), yet their mortality decreased (-1.0%). Their average annual bed-days consumption also increased (+15,004 and +1,437 in non-intensive care wards and ICU, respectively). CONCLUSIONS: Since 2014, older adults have represented the majority of admissions in Level-I trauma centers in Québec. Their bed-days consumption has greatly increased, and their injury pattern and severity have deeply evolved, while we showed a decrease in 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".