Incidence, Distribution, and Cost of Lawn-Mower Injuries in the United States, 2006-2013
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it