Surveillance of hospitalized farm injuries in Canada
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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