A Comparison of Manual versus Electric Bicycle Injuries Presenting to a Pediatric Emergency Department
Bibliographic record
Abstract
BACKGROUND: The use of electric bicycles (E-bikes) has dramatically increased over the last decade. E-bikes offer an inexpensive, alternative form of transport, but also pose a new public health challenge in terms of safety and injury prevention. OBJECTIVE: The aim of this study was to describe the epidemiology and severity of E-bike related injuries among children treated in the emergency department (ED) and to compare these to manual bicycle related injuries. METHODS: A retrospective observational study of all pediatric patients presenting to the ED between December 2014 and November 2015 with an injury related to E-bike or manual bicycle use. Data including demographics, diagnosis, injury severity score (ISS), and outcome were compared. RESULTS: A total of 196 cyclist injuries presented to the ED; 85 related to E-bike use and 111 to manual bicycle riders. The mean age of E-bikers was 13.7 years (7.5-16 years) and of manual bicycle riders was 9.9 years (3-16 years). Injuries to the head and the extremities were common in both groups. E-bikers had significantly more intra-abdominal organ injury (P=0.047). Injury severity scores were low overall, but injuries of higher severity (ISS>9) only occurred among the E-bikers. CONCLUSIONS: Pediatric E-bike injuries tend to be more severe than those sustained during manual bicycle riding. Further research into bicycle and other road and pavement users could lead to enhanced regulation regarding E-bike usage.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".