Elderly drivers and emergency department visits
Bibliographic record
Abstract
Canadians are living longer and have more active lifestyles, which means that there are more elderly drivers and increasing road traffic injuries among this population. The primary objective of this study was to examine the injury profile and emergency healthcare utilization of older drivers (age =70) treated in an emergency department (ED) after a motor vehicle crash (MVC). We conducted a retrospective cross-sectional study with data obtained by chart reviews of MVC related ED visits at Vancouver General Hospital, British Columbia from January 1, 2009 to May 31, 2014. Each older injured driver (age =70) was matched with 2 younger injured drivers (age <70) with the closest ED visit date and time. Data from medical records including demographics, crash circumstances, injury profile and ED care information were analysed using descriptive and logistic regression analyses. Elderly drivers were 2.41 times more likely to be admitted to hospital and sustained more injury after a crash compared to younger drivers. As expected, elderly drivers also had longer ED length of stay compared to younger drivers (median time: 184.5 versus 155 minutes) as well as higher rates of ambulance arrival (85.4% versus 68.3%), blood work requirement (63.5% versus 39.3%) and diagnostic imaging (78.1% versus 62.8%). Compared to younger drivers, elderly drivers involved in MVCs sustain more serious injuries and have higher healthcare utilization. These indicate the need for programs to identify at risk elderly drivers and ED treatment protocol to care for the increasing older driving population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".