901 Risk factors for bicycling injuries in children and adolescents: a systematic review
Bibliographic record
Abstract
Background Bicycling injuries in young people represent a substantial cost to health care systems. The objective of this review was to examine the individual and environmental factors associated with bicycling-related injury risk in children and youth. Methods Fourteen electronic databases were searched using exploded subject headings and keywords. Two authors independently screened article titles and abstracts for inclusion. The full-text of the potential articles was assessed to determine eligibility. The inclusion criteria were bicyclists less than 18 years old; individual and environmental characteristics of bicycling outcomes; comparisons between injured and uninjured bicyclists, injury type or severity level; study designs with a pre-determined comparison group; and publications in English from 1990 to May 2015. The exclusion criteria were injury outcomes related to helmet use, helmet legislation or mountain biking; comparisons of census-based injury rates; cross-sectional studies; and letters to the editor. A modified version of the Newcastle-Ottawa Scale was used to assess study quality. Results Fifteen articles met the inclusion and exclusion criteria. Overall, 46 different risk factors were examined. The most commonly reported risk factors were age (N = 10 studies), sex (N = 7), equipment related factors (N = 6), bicycling exposure (N = 5), bicycling purpose (N = 5), and motor vehicle (MV) collision (N = 4). The reviewed studies varied in quality; the main weaknesses were inadequate definitions of study groups, lack of control for potential confounders, and the use of self-reported data. Conclusions While many of the studies had significant limitations, one recurring theme was that young bicyclists received more severe injuries when exposed to MV collisions. To reduce injuries in children and adolescents, we recommend separating bicyclists from MVs on the road and implementing strategies to reduce traffic speed and volume.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".