Characterizing all-terrain vehicle (ATV)-related harm in Saskatchewan children and youths: A retrospective chart review of patients presenting to hospitals in Saskatoon, Canada from 2016 to 2021
Bibliographic record
Abstract
Objectives: All-terrain vehicles (ATVs) are a leading cause of serious injury in children. There are limited Saskatchewan-specific data on these injuries. We aimed to characterize ATV-related injuries in children and youths under the age of 20 in Saskatchewan. Methods: A retrospective chart analysis of the 354 patients presenting to hospitals in Saskatoon for ATV-related injuries between 2016 and 2021. Results: Incidents commonly occurred in male patients (70%) aged 12 to 16 (45%), riding a quad (as opposed to a dirt bike or other ATV) (36%) between May and August (68%). Fractures were the most common injury (50%), though moderate/severe head injuries (6%) and polytrauma (6%) were documented. The type of injury was significantly associated with the patient's health region of residence, with Saskatoon patients accounting for the highest proportion of fractures (47%), mild head injuries (71%), and soft tissue injuries (62%), while patients from the North and Far North Saskatchewan accounted for the highest proportion of moderate/severe head injuries (76%) and polytrauma (50%). Most incidents involved quads (36%), and these incidents were the most severe (14% moderate/severe head injuries and polytrauma). 48% of patients wore helmets when using four-wheeled ATVs compared with 91% of patients on dirt bikes, and this difference may account for only 2% of dirt bike incidents resulting in moderate/severe head injuries and polytrauma. Conclusions: Findings suggest a false perception of safety regarding four-wheeled ATVs. Targeted public education initiatives are needed in Saskatchewan to address ATV-related risks to promote safer riding behaviours and reduce morbidity among children and youths.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".