Fatal and non‐fatal machine‐related injuries suffered by children in Alberta, Canada, 1990–1997
Bibliographic record
Abstract
BACKGROUND: Children raised on farms are exposed to many hazardous types of machinery. The objective of this study was to describe the magnitude of non-fatal and fatal farm machinery injuries in Alberta children and adolescents (0-17 years) for the years 1990-1997. To date, there have been no published studies of pediatric farm injuries in Western Canada. METHODS: Data were collected through the Canadian Agricultural Injury Surveillance Program (CAISP). Death certificates and hospital charts were audited to provide enhanced information about the circumstances of injuries related to farm machinery for farm persons aged 17 years and younger. RESULTS: A total of 302 farm machinery injuries were recorded for the years 1990-1997. Of these, 14 resulted in death. All-terrain vehicles (ATVs) were the most common cause of injury (n = 76), followed by tractors (n = 72), and power take-offs (n = 15). The predominant injury mechanism was entanglement (n = 69), followed by falls from machines (n = 57), and being pinned/struck by a machine (n = 49). The median length of hospital stay for injuries was 2.0 days. Males (median = 2.0 days) had significantly longer hospital stays than females (median = 1.0 days). There were significantly more injuries reported during the summer and autumn than during the winter and spring. Those injured in the autumn were significantly older (median = 13.0 years) than children injured in the spring (median = 9.0 years). Injury rates dropped significantly during the study period from 119.9/100,000 per year in 1990 to 50.7/100,000 in 1997. CONCLUSIONS: While injury rates have dropped, the number of injuries occurring to children on Alberta farms is of concern. The large number of ATV related injuries suggests that preventative strategies need to be focused in this area. Am. J. Ind. Med. 45:177-185, 2004.
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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) | 0.001 | 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 | 0.001 |
| 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".