The impact of public health lockdown measures during the COVID-19 pandemic on the epidemiology of children’s orthopedic injuries requiring operative intervention
Bibliographic record
Abstract
Background: In March 2020, Ontario instituted a lockdown to reduce spread of the SARS-CoV-2 virus. Schools, recreational facilities, and nonessential businesses were closed. Restrictions were eased through 3 distinct stages over a 6-month period (March to September 2020). We aimed to determine the impact of each stage of the COVID-19 public health lockdown on the epidemiology of operative pediatric orthopedic trauma. Methods: A retrospective cohort study was performed comparing emergency department (ED) visits for orthopedic injuries and operatively treated orthopedic injuries at a level 1 pediatric trauma centre during each lockdown stage of the pandemic with caseloads during the same date ranges in 2019 (prepandemic). Further analyses were based on patients’ demographic characteristics, injury severity, mechanism of injury, and anatomic location of injury. Results: Compared with the prepandemic period, ED visits decreased by 20% (1356 v. 1698, p < 0.001) and operative cases by 29% (262 v. 371, p < 0.001). There was a significant decrease in the number of operative cases per day in stage 1 of the lockdown (1.3 v. 2.0, p < 0.001) and in stage 2 (1.7 v. 3.0; p < 0.001), but there was no significant difference in stage 3 (2.4 v. 2.2, p = 0.35). A significant reduction in the number of playground injuries was seen in stage 1 (1 v. 62, p < 0.001) and stage 2 (6 v. 35, p < 0.001), and there was an increase in the number of self-propelled transit injuries (31 v. 10, p = 0.002) during stage 1. In stage 3, all patient demographic characteristics and all characteristics of operatively treated injuries resumed their prepandemic distributions. Conclusion: Provincial lockdown measures designed to limit the spread of SARS-CoV-2 significantly altered the volume and demographic characteristics of pediatric orthopedic injuries that required operative management. The findings from this study will serve to inform health system planning for future emergency lockdowns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".