Animal–vehicle collisions in Victoria, Australia: An under‐recognised cause of road traffic crashes
Bibliographic record
Abstract
OBJECTIVE: Non-fatal injuries sustained from animal-vehicle collisions are a globally under-recognised road safety issue, with limited data on these crash types. The present study aimed to quantify the number and causes of major trauma events resulting from animal-vehicle collisions. METHODS: The study was a retrospective analysis of major trauma cases occurring in Victoria, Australia, between 2007 and 2016, using data from the population-based Victorian State Trauma Registry. To identify animal-vehicle collisions, Victorian State Trauma Registry injury codes were combined with text-mining of the text description of the injury event. RESULTS: Over the 10 year period, there were 152 major trauma patients who were admitted to Victorian trauma-receiving hospitals due to vehicle collisions with animals. The crude population-based incidence rate for animal-vehicle collisions increased by 6.7% per year (incidence rate ratio 1.07; 95% confidence interval 1.01-1.13; P = 0.02). CONCLUSION: Development of systematic recording methods of animal-vehicle collisions will improve reporting of these crash types to assist future studies in implementing effective countermeasures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".