Bicycle injuries presenting to the emergency department during <scp>COVID</scp>‐19 lockdown
Bibliographic record
Abstract
AIM: Since the start of the COVID-19 pandemic, there have been many changes in the presenting complaints in paediatric emergency departments (EDs). We sought to characterise the impact of the COVID-19 pandemic on bicycle-related injuries in children presenting to a tertiary care paediatric ED. METHODS: We conducted a descriptive, cross-sectional study of ED visits to a large urban tertiary children's hospital, comparing March to October 2020 (the study period) to the same date range 2 years prior (i.e. March to October 2018-2019). We included children 0-17.99 years presenting for a bicycle-related injury. We compared absolute visit counts of bike injuries per month, demographics, triage acuity, injury type and disposition. RESULTS: A total of 1215 bike-related visits were analysed. There were 234 presentations in 2018 (March to October), 305 in 2019, and 676 in 2020. Overall, the mean age was 9.5 years (standard deviation 5.5-13.5), there were 67% males, median Canadian Emergency Department Triage and Acuity Scale score was 3 (interquartile range 3-4) and the most common injuries were fractures (n = 471, 38.8%). There were significantly more bike injuries presenting to the ED per month in the COVID group, 33.7(17.9) versus 84.5(61.4) (two-tailed P value = 0.041). There was no statistical difference in 'severe injuries' pre- versus post-COVID (odds ratio 0.815 (95% confidence interval 0.611-1.088), P = 0.165). CONCLUSION: There was a significant increase in bicycle-related injuries presenting to our ED during the pandemic, compared to previous years. Evaluating these trends will allow for the exploration of harm reduction strategies for preventing future bicycle-related injuries.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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, 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".