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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".