Vulnerability to pedestrian trauma: Demographic, temporal, societal, geographic, and environmental factors
Bibliographic record
Abstract
Background: Pedestrian trauma frequently results in devastating and costly injuries and accounts for 11% of all road-user fatalities. Trauma surgeons, with their unique perspectives on and access to injury data, and geographers, with their access to powerful spatial analysis tools, are in a strong position to help in the development of relevant and evidence-based policy for the prevention of pedestrian trauma. Methods: A 5-year retrospective co hort study (2001 to 2006) was conducted in a large metropolitan region to determine demographic, temporal, societal, geographic, and environmental risk factors for pedestrian trauma. Results: A total of 387 patients sustained severe injuries as a result of pedestrian trauma during the study period. Fifty-five percent (214/387) were male and the mean age of all patients was 54.0 years (range 18.0– 98.0; SD 20.0). Most injuries tended to occur in the early evening and during the fall months (September to November). Inhabitants of lower socioeconomic status neighborhoods were particularly vulnerable and incidents appeared to cluster in high-risk locations. Conclusions: Combined epidemiological and geospatial analyses can provide insights into a broad array of risk factors for pedestrian trauma. Spatial epidemiology may also have applications for other public health issues with complex determinants. Background The consequences of motor vehicle crashes involving pedestrians are devastating. Canadian injury statistics from 1992 to 2001 reveal a yearly average of 416 fatalities and 14 252 injuries from pedestrian trauma (PT). 1
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".