Bibliographic record
Abstract
BACKGROUND: Agricultural work involves hazards that may harm long-term well-being. We evaluated the risk of long-term disability and death for agricultural workers compared to construction workers with similar demographics. We hypothesized that delays to emergency care and subsequent long-term disability following injury might be worse for agricultural workers compared to those injured in construction. METHODS: We evaluated all adults severely injured on farms or on construction sites in Ontario, Canada, between April 1, 2009, and March 31, 2012, according to the Ontario Trauma Registry. We excluded individuals living outside of the province, those missing a valid health card number, or youth less than 17 years old. Our primary outcome was death or the subsequent application for disability support. RESULTS: In total, 353 patients were injured on a farm or construction site during the study period. Delays to emergency care exceeding 12 hours were more frequent for agricultural workers compared to construction workers (43% vs 23%, P <.001). After a 5-year follow-up, agricultural workers had a death or disability rate marginally higher than construction workers (23% vs 14%, P = .068), equivalent to a hazard ratio of 1.62 that was marginally statistically significant (95% confidence interval 0.96-2.75, P = .072). The risk of death and disability was greatest for patients who had the longest delays to emergency care. INTERPRETATION: Agricultural workers experience a substantial delay in receiving emergency care and a marginally higher risk of death or disability in the years following injury compared to construction workers.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".