Incidence and severity of paediatric sledding injuries during the COVID-19 pandemic
Bibliographic record
Abstract
Objectives: During the coronavirus disease 2019 (COVID-19) pandemic, we observed an increase in sledding injuries resulting in paediatric emergency department (PED) visits. We sought to describe the potential impact of the pandemic on the incidence and severity of sledding injuries in children. Methods: We conducted a descriptive, retrospective cohort study of children with sledding injuries presenting to the PED in a tertiary care centre. We compared injuries occurring before and after the start of the pandemic, using March 11, 2020 as a cut-off. Children aged 0 to 17 years presenting with sledding injuries were eligible for inclusion. We compared monthly visit counts of sledding injuries, demographics, site and type of injury, disposition, surgical intervention, return visits, and length of inpatient admissions. Results: In total, 243 visits for sledding injuries were analysed. There were 13 presentations in 2018, 31 in 2019, 30 in 2020, 105 in 2021, and 64 in 2022. The mean age was 8.6 years (standard deviation 3.7), with 53.5% of injuries occurring in males. Head injuries and sprains/strains were the most common site and type of injury, respectively (n = 103, 41.4%). There was a significant increase in the number of sledding injuries between pre-COVID-19 and COVID-19 years (P = 0.048). There were no significant differences in demographic and injury characteristics. Conclusions: We identified a significant increase in PED visits for sledding injuries during the pandemic. Examining epidemiological trends of sledding injuries can facilitate advocacy for improved communication of injury prevention recommendations in the event of future infectious disease outbreaks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".