Comparison of the number of pedestrian and cyclist injuries captured in police data compared with health service utilisation data in Toronto, Canada 2016–2021
Bibliographic record
Abstract
INTRODUCTION: Pedestrian and cyclist injuries represent a preventable burden to Canadians. Police-reported collision data include information on where such collisions occur but under-report the number of collisions. The primary objective of this study was to compare the number of police-reported collisions with emergency department (ED) visits and hospitalisations in Toronto, Canada. METHODS: Police-reported collisions were provided by Toronto Police Services (TPS). Data included the location of the collision, approximate victim age and whether the pedestrian or cyclist was killed or seriously injured. Health services data included ED visits in the National Ambulatory Care Reporting System and hospitalisations from the Discharge Abstract Database using ICD-10 codes for pedestrian and cycling injuries. Data were compared from 2016 to 2021. RESULTS: Injuries reported in the health service data were higher than those reported in the TPS for cyclists and pedestrians. The discrepancy was the largest for cyclists treated in the ED, with TPS capturing 7.9% of all cycling injuries. Cyclist injuries not involving a motor vehicle have increased since the start of the pandemic (from 3629 in 2019 to 5459 in 2020 for ED visits and from 251 in 2019 to 430 for hospital admissions). IMPLICATIONS: While police-reported data are important, it under-reports the burden. There have been increases in cyclist collisions not involving motor vehicles and decreases in pedestrian injuries since the start of the pandemic. The results suggest that using police data alone when planning for road safety is inadequate, and that linkage with other health service data is essential.
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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".