Using injury severity to improve occupational injury trend estimates
Bibliographic record
Abstract
BACKGROUND: Hospitalization-based estimates of trends in injury incidence are also affected by trends in health care practices and payer coverage that may differentially impact minor injuries. This study assessed whether implementing a severity threshold would improve occupational injury surveillance. METHODS: Hospital discharge data from four states and a national survey were used to identify traumatic injuries (1998-2009). Negative binomial regression was used to model injury trends with/without severity restriction, and to test trend divergence by severity. RESULTS: Trend estimates were generally biased downward in the absence of severity restriction, more so for occupational than non-occupational injuries. Restriction to severe injuries provided a markedly different overall picture of trends. CONCLUSIONS: Severity restriction may improve occupational injury trend estimates by reducing temporal biases such as increasingly restrictive hospital admission practices, constricting workers' compensation coverage, and decreasing identification/reporting of minor work-related injuries. Injury severity measures should be developed for occupational injury surveillance systems.
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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 | 0.028 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".