Circumstances and outcome of active transportation injuries: protocol of a British Columbian inception cohort study
Bibliographic record
Abstract
INTRODUCTION: Active transport (AT) is promoted by urban planners and health officials for its environmental, economic and societal benefits and its uptake is increasing. Unfortunately, AT users can be injured or killed due to falls or collisions. Active transport injury (ATI) prevention efforts are hindered by limited research on the circumstances, associated infrastructure, injury pattern, severity and outcome of ATI events. This study seeks to address these knowledge gaps by identifying built environment features associated with injury and risk factors for a poor outcome following ATI. METHODS AND ANALYSIS: This prospective observational study will recruit an inception cohort of 2000 ATI survivors, including pedestrians, cyclists and micromobility users aged 16 years and older who arrive at a participating emergency department within 48 hours of sustaining an ATI. Baseline interviews capture demographic and socioeconomic information, pre-injury health and functional status, as well as circumstances of the injury event and recovery expectations. Follow-up interviews at 2, 4, 6 and 12 months postinjury (key stages of recovery) use standardised health-related quality of life tools to determine physical and mental health outcomes, functional recovery and healthcare resource use and lost productivity costs. ETHICS AND DISSEMINATION: The Active Transportation Injury Circumstances and Outcome Study is approved by our institutional research ethics board and the research ethics boards of all participating sites. This study aims to provide healthcare providers with knowledge of risk factors for poor outcome following ATI with the goal of improving patient management. Additionally, this study will provide insight into the circumstances of ATI events including built environment features and how those circumstances relate to recovery outcomes. This information can be used to inform city engineers and planners, policymakers and public health officials to plan roadway design and injury prevention policy.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".