Incidence, Distribution, and Cost of Lawn-Mower Injuries in the United States, 2006-2013
Bibliographic record
Abstract
OBJECTIVES: Characterization of the epidemiology and cost of lawn-mower injuries is potentially useful to inform injury prevention and health policy efforts. We examined the incidence, distribution, types and severity, and emergency department (ED) and hospitalization charges of lawn-mower injuries among all age groups across the United States. METHODS: This retrospective, cross-sectional study used nationally representative, population-based (all-payer) data from the US Nationwide Emergency Department Sample for lawn-mower-related ED visits and hospitalizations from January 1, 2006, through December 31, 2013. Lawn-mower injuries were identified by using International Classification of Diseases, Ninth Revision, Clinical Modification code E920 (accidents caused by a powered lawn mower). We analyzed data on demographic characteristics, age, geographic distribution, type of injury, injury severity, and hospital charges. RESULTS: We calculated a weighted estimate of 51 151 lawn-mower injuries during the 8-year study period. The most common types of injuries were lacerations (n = 23 907, 46.7%), fractures (n = 11 433, 22.4%), and amputations (n = 11 013, 21.5%). The most common injury locations were wrist or hand (n = 33 477, 65.4%) and foot or toe (n = 10 122, 19.8%). Mean ED charges were $2482 per patient, and mean inpatient charges were $36 987 per patient. The most common procedures performed were wound irrigation or debridement (n = 1436, 29.9%) and amputation (n = 1230, 25.6%). CONCLUSIONS: Lawn-mower injuries occurred at a constant rate during the study period. Changes to nationwide industry safety standards are needed to reduce the frequency and severity of these preventable 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".