Characteristics of outdoor motorized scooter-related injuries: analysis of data from the electronic Canadian Hospitals Injury Reporting and Prevention Program (eCHIRPP)
Bibliographic record
Abstract
INTRODUCTION: The use of motorized scooters is gaining popularity in Canada and elsewhere. This study aims to summarize characteristics of injuries related to use of motorized scooters using data from the electronic Canadian Hospitals Injury Reporting and Prevention Program (eCHIRPP) and to analyze trends. The eCHIRPP collects information associated with the injury event and clinical information related to treatment (the injured body part, the nature of the injury, injury intent and treatment received) from 11 pediatric and 9 general hospitals across Canada. RESULTS: A free-text search using keywords identified 523 cases related to motorized scooter injuries between January 2012 and December 2019. Most of the injuries reported were among males (62.7%). Fracture/dislocation was the most frequent injury (36.9%), and 14.3% of all patients were admitted to hospital. Joinpoint regression showed a statistically significant increase in injuries related to motorized scooter use between 2012 and 2017 (annual percent change of 18.4%). CONCLUSION: Study findings indicate the need for continued preventive efforts and improved educational messages on safe riding and the importance of the use of protective equipment to prevent injuries among riders.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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, 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".