Occupational exposures and IPF: when the dust unsettles
Bibliographic record
Abstract
Objective: To compare injury case fatality rates in the United States (US) with New Zealand (NZ) to guide future information collection, research, and evaluation. Design: Using NZ (1992–96) and US (1996–98) mortality censuses, NZ national 1992–96 hospital discharge censuses, and US 1996–98 National Hospital Discharge Survey data, the authors compared case fatality rates by mechanism and intent of injury and age group. The analysis was restricted to severe injuries (AIS⩾3). Subjects: NZ (1992–96) and US (1996–98) populations. Main outcome measures: Ratio of case fatality rates in NZ versus the US (RCFR(NZ:US)). Results: Overall, among cases meeting the study criteria, unintentional injuries were 1.57 times more likely fatal in NZ and intentional assault injuries were 1.14 times more likely to be fatal in the US. Firearms were involved in 50% of US assaults versus 8% of NZ assaults. By mechanism, cutting/piercing injuries were 1.86, firearm injuries were 1.41, and motor vehicle injuries were 1.44 times more to be likely fatal in NZ. Natural/environmental injuries (RCFRNZ:US = 0.57), unintentional poisonings (RCFRNZ:US = 0.26), and unintentional suffocations (RCFRNZ:US = 0.67) were significantly more likely to be fatal in the US. Conclusions: Possible reasons for the observed results include: differences in geography and proportion of population in rural areas, trauma system differences, road design and vehicle types, seat belt use, larger role of firearms in US assaults, coding practices, policies, and environmental factors. Disparities evoke hypotheses to test in future research that will guide priority setting and intervention.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".