Epidemiology, injury pattern and outcome of older Canadian trauma patients: a 15-year study of a provincial trauma registry
Bibliographic record
Abstract
Abstract IntroductionOlder adults have become a significant portion of the trauma population. 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. MethodsA multicentre retrospective study was conducted using the Quebec Trauma Registry. Patients aged ≥16 years, admitted to one of the three adult level-I trauma centre between 2003 and 2017 were included. Descriptive analyses were performed.ResultsWe included 53,324 patients and 24,822 were aged ≥65 years. The median age increased from 57[IQR 36-77] to 67[IQR 46-82] years and the proportion of older adults rose from 41.8% to 54.1%. Among those, fall remained the main mechanism (84.7%-88.3%) and the proportion of severe thorax (+8.9%), head (+8.7%), and spine (+5%) injuries increased over time. The proportion of severely injured older patients almost doubled (17.6%-32.3%), yet their mortality decreased (-1%). Their average number of annual bed-days consumption also increased (+15,004 and +1,437 in non-intensive care ward and ICU, respectively).ConclusionsSince 2014, older adults represent the majority of admissions in Level-I trauma centres in Québec. Their bed-days consumption has greatly increased, their injury pattern and severity has 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".