Hospitalized Head and Spine Injuries on Saskatchewan Farms
Bibliographic record
Abstract
INTRODUCTION: With over 44,000 individual farms, farm dwellers account for 11% of the population of Saskatchewan. There is limited data on brain and spine injuries acquired on farms. The objective of this study was to evaluate the epidemiology of head and spine injuries on Saskatchewan farms to assist the development of injury prevention initiatives. METHODS: Using the Canadian Centre for Agricultural Health and Safety's Saskatchewan Farm Injury Surveillance Database, farm-related head and spine injuries hospitalized > 24 hours were examined (1990-2007). We collected information regarding the type and mechanism of injury as well as the geographic location of both the injury and treatment. RESULTS: The database captured 390 brain injuries and 228 spine injuries, including 16 spinal cord injuries. The majority of patients were male (73.3% of head injuries and 84.2% of spine injuries). The highest risk age groups were 50-59 years, with 24.1% of the spine injuries, and 40-49 years, with 19.2% of the head injuries. The most common causes of injury were falls and/or machinery-related. The average annual incidence of farm-related spine and head injury were 10.8 and 17.6 per 100,000 farm population, respectively. All patients included in this study were hospitalized for over 24 hours, with 44.7% of spine injuries spending over one week in hospital, and 20% of head injuries spending over three days in hospital. CONCLUSIONS: Injury prevention initiatives should be targeted towards males aged 40-59 years residing in the southern areas of the province, with increased awareness towards the dangers of falls and operating tractors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".