Surveillance of hospitalized farm injuries in Canada
Bibliographic record
Abstract
OBJECTIVE: To provide an overview of hospital admissions for the treatment of farm injuries. DESIGN: descriptive analysis of data from the Canadian Agricultural Injury Surveillance Program (CAISP). POPULATION: persons experiencing a farm injury requiring hospitalization, April 1991 to March 1995. Access to hospital separation data was negotiated within Canadian provinces. Individual cases were verified by medical records personnel and supplemental data describing injury circumstances were obtained. ANALYSIS: descriptive analyses characterizing farm injuries by: persons involved, mechanisms, primary diagnoses, and agents of injury. RESULTS: Data from 8/10 Canadian provinces representing 98% of the farm population were obtained. A total of 8,263 farm injuries were verified. Adults aged 60 years and older were over-represented in these injuries. Leading external causes of agricultural machinery injury included entanglements, being pinned/struck by machinery, falls, and runovers. Non-machinery causes included falls from heights, animal related trauma, and being struck/by against objects. Leading diagnoses varied by age group, but included: limb fractures/open wounds, intracranial injuries, skull fractures, and spinal/ truncal fractures. CONCLUSIONS: CAISP is a new agricultural injury surveillance program in Canada. Data from this system are actively used to inform prevention initiatives, and to indicate priorities for etiological and experimental research in the Canadian agricultural setting.
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".