Are outdoor playgrounds the real culprit for elbow fractures in children? A lesson learned from COVID-19 sanitary measures
Bibliographic record
Abstract
BACKGROUND: The association between elbow fractures and outdoor playgrounds has always been anecdotal. We sought to determine the impact of closing outdoor playgrounds and other play areas during the COVID-19 lockdown on elbow fractures in a pediatric population. METHODS: We conducted a retrospective cohort study of all elbow fractures from a single pediatric referral hospital between 2016 and 2020 for the months of April and May. The months chosen corresponded to the COVID-19 lockdown during which outdoor playgrounds were closed. Inclusion criteria were elbow fracture diagnosis based on radiography and age younger than 18 years. Fracture type, where the injury occurred and the mechanism of injury were recorded. RESULTS: A total of 370 fractures were reported, with an average of 83 (95% confidence interval [CI] 83-84) per year for 2016-19 and only 36 recorded in 2020. The average annual number of fractures before 2020 was 17 (95% CI 16-17) for schools, and 33 (95% CI 31-34) for outdoor playgrounds, including 22 (95% CI 21-24) falls from playground structures. No fracture was reported in schools in 2020, and only 3 were reported from outdoor playgrounds (including 1 associated with falling from playground structures). CONCLUSION: We found an association between elbow fractures in a pediatric population and outdoor playground accessibility, but also with indoor public locations. Our findings emphasize the importance of safety measures in those facilities.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".