Lawnmower Injuries in Children: A National 13-Year Study of Urban Versus Rural Injuries
Bibliographic record
Abstract
INTRODUCTION: Although the American Academy of Orthopaedic Surgery, American Academy of Pediatrics, and Pediatric Orthopedic Society of North America have established lawnmower safety guidelines, a notable number of injuries continue to occur. We sought to elaborate on the epidemiology of lawnmower injuries in the pediatric age group and compare urban versus rural injuries. METHODS: The Pediatric Health Information System database was queried for patients of 1 to 18 years of age from 2005 to 2017 who presented with a lawnmower injury. Results were computed using bivariate tests and multinomial regressions. RESULTS: A total of 1,302 lawnmower injuries were identified (mean age 7.7 ± 5.1 years, range 1 to 18 years; 78.9% males). Incidence rates by region, adjusted for regional case volume, were 2.16 injuries per 100,000 cases in the South, 2.70 injuries per 100,000 cases in the Midwest, 1.34 injuries per 100,000 cases in the Northeast, and 0.56 injuries per 100,000 cases in the Western United States. After stratifying and adjusting for total case volume by locale (urban/rural), it was found that urban areas had an incidence rate of 1.47 injuries per 100,000 cases, whereas rural areas had a rate of 7.26 injuries per 100,000 cases. Rural areas had higher rates of infection and higher percentages of patients requiring inpatient stay. The surgical complication rate in rural areas was 5.5% as compared to 2.6% in urban areas. Based on urban/rural status, a significant difference was observed with the age group, length of stay, income, surgical complication, and presence of infection at the bivariate level with P < 0.05. Rural areas had an overall amputation rate of 15.5% compared with 9.6% in urban areas, with rural patients being 1.7 times more likely to undergo an amputation (P < 0.05). CONCLUSION: The findings of this study show that numerous geographic and locale disparities exist in pediatric lawnmower injuries and reveal the need for improved safety awareness, especially in at-risk rural populations.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".