Demographic and clinical profile of an inception cohort of road trauma survivors
Bibliographic record
Abstract
BACKGROUND: Road trauma is a major public health concern, often resulting in reduced health-related quality of life and prolonged absenteeism from work even after so-called 'minor' injuries that do not result in hospitalization. This manuscript compares pre-injury health, sociodemographic characteristics and injury details between age, sex, and road user categories in a cohort of 1,480 road trauma survivors. METHODS: This was a prospective observational inception cohort study of road trauma survivors recruited between July 2018 and March 2020 from three trauma centres in British Columbia, Canada. Participants were aged ≥ 16 years and arrived in a participating emergency department within 24 h of involvement in a motor vehicle collision. Data were collected from structured interviews and review of medical records. RESULTS: The cohort of 1,480 road trauma survivors included 280 pedestrians, 174 cyclists, 118 motorcyclists, 683 motor vehicle drivers, and 225 passengers. Median age was 40 (IQR = [27, 57]) years; 680 (46%) were female. Males and younger patients were significantly more likely to report better pre-injury physical health. Motorcyclists and cyclists tended to report better physical health and less severe somatic symptoms, whereas pedestrians and motor vehicle drivers reported better mental health. Injury severity and hospital admission rates were higher in pedestrians and motorcyclists and lower in motorists. Upper and lower extremity injuries were most common in pedestrians, cyclists and motorcyclists, whereas neck injuries were most common in motor vehicle drivers and passengers. CONCLUSIONS: In a large cohort of road trauma survivors, overall injury severity was low. Motorcyclists and pedestrians, but not cyclists, had more severe injuries than motorists. Extremity injuries were more common in vulnerable road users. Future research will investigate one-year recovery outcomes and identify risk factors for poor recovery.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".