123 Single-center report of hospitalized children with unintentional injuries in the first quarter before and after the epidemic
Bibliographic record
Abstract
<h3>Background</h3> The COVID-19 epidemic prevention and control policies implemented in China are globally recognized for their unparalleled stringency and enduring nature. <h3>Objective</h3> This study compared the characteristics of pediatric patients hospitalized for unintentional injuries before and after the epidemic, and assessed the impact of the epidemic on the incidence of unintentional injuries in children. <h3>Methods</h3> A comparative cross-sectional study was conducted on the medical data of 1267 children with accidental injuries who were hospitalized in the Children’s Hospital of Fudan University from January to March, between 2017 and 2021. An interrupted time series analysis was conducted to estimate the impact of the COVID-19 pandemic on hospitalizations for unintentional injuries among children in a single center. The study divided children into two groups based on the timing of the outbreak: a pre-pandemic group (2017–2019) and a post-pandemic group (2020–2021). A comparison was made between these two groups regarding demographic characteristics, trauma mechanisms, duration of visits, hospitalization rates, and prognosis. <h3>Results</h3> An interrupted time series model has projected a significant reduction in cases attributable to the COVID-19 pandemic, indicating a shortfall of 26%.Furthermore, there were significant increases in the time intervals from injury to surgery (P<0.001). However, no statistical differences were found in terms of hospitalization duration and prognosis between the two periods. Statistically significant differences were observed between the two groups of children regarding injury location, mechanism, and structure (P<0.001, P=0.003, P<0.001). Following the onset of the epidemic, families (38.28%) became the primary setting for accidental injuries among children instead of public places before the outbreak (40.24%). Low-energy falls remained the main cause of injuries (42.29%), while hospitalizations due to foreign objects, animal bites, firearm injuries, drowning, suffocation, and poisoning did not occur during this period. However, there was an increased proportion of polytrauma cases compared to pre-epidemic levels (3.14% vs 0.43%, P<0.001). <h3>Conclusion</h3> In the context of COVID-19 infection, there has been a significant increase in accidental injuries among children’s families, with falls remaining the main cause. Despite this prolonged pre-hospital preparation period, patients‘ prognosis remains comparable, reflecting the hospital’s level of medical expertise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".