Ascertainment and description of pedestrian and bicycling injuries and fatalities in Ontario from administrative health records 2003–2017: contributions of non-collision falls and crashes
Bibliographic record
Abstract
INTRODUCTION: Pedestrian and bicycling injuries may be less likely to be captured by traffic injury surveillance relying on police reports. Non-collision injuries, including pedestrian falls and single bicycle crashes, may be more likely than motor vehicle collisions to be missed. This study uses healthcare records to expand the ascertainment of active transportation injuries and evaluate their demographic and clinical features. METHODS: We identified pedestrian and bicyclist injuries in records of deaths, hospitalisations and emergency department visits in Ontario, Canada, between 2002 and 2017. We described the most common types of clinical injury codes among these records and assessed overall counts and proportions of injury types captured by each ascertainment definition. We also ascertained relevant fall injuries where the location was indicated as 'street or highway'. RESULTS: Pedestrian falls represented over 50% of all pedestrian injuries and affected all age groups, particularly non-fatal falls. Emergency department records indicating in-traffic bicycle injuries not involving a collision with motor vehicles increased from 14% of all bicycling injury records in 2003 to 34% in 2017. The overall number of injuries indicated by these ascertainment methods was substantially higher than official counts derived from police reports. CONCLUSION: The use of healthcare system records to ascertain bicyclist and pedestrian injuries, particularly to include non-collision falls, can more fully capture the burden of injury associated with these transportation modes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".