Detailed Analysis of Pedestrian Casualty Collisions in Victoria, Australia
Bibliographic record
Abstract
OBJECTIVE: Pedestrian road trauma is significant in Australia and requires in-depth understanding to improve or inform new countermeasures. Analyses on single data sources can be limited. This study investigated demographic, behavioral, environmental, and collision characteristics of pedestrian injury in Victoria, Australia, over a 5-year period using multiple data sources. METHODS: Victorian state police, hospital presentation, hospital admission, and coronial data sets were analyzed and compared for the years 2004 to 2008. RESULTS: Analyses identified 3,702 police-recorded pedestrian casualties (deaths and injuries, of which 256 were deaths), 5,008 pedestrian traffic-related hospital presentations, and 2,802 pedestrian admissions. Trend analyses showed significant increases in police casualty and hospitalization rates per 100,000 population. Age groups most commonly involved were those aged 18-24 especially on weekends, 75+ especially on weekday days, and 13- to 17-year-olds especially at school commute times. Proportionally more cases were male in all data sets. One quarter of coroner-examined deaths involved alcohol and one third involved drugs. Two thirds of police-recorded casualties occurred on weekdays, and 45% of weekend casualties occurred at night. Most casualties occurred in urban areas (95%), in lower-speed zones (78%); however, 79% of rural casualties occurred in high-speed zones, of which more were fatal. Over half did not occur at intersections. The most common injuries were fractures as well as multiple injuries, which together with intracranial injuries, were most common among fatalities (50 and 34%, respectively). Serious injury was more likely in older pedestrians, in males, in rural areas, in 60-80 km/h zones, in areas with poor lighting, while crossing a carriageway, not at an intersection, and when struck by a heavy vehicle. CONCLUSIONS: Findings indicate pedestrian serious injury rates are increasing and identify targets for countermeasures. Inherent limitations present in each relevant data collection require mutliple data sets to be explored and results contrasted. Jurisdictions seeking to determine pedestrian injury risk factors should aim to link police and hospital data for a complete analysis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".