Improving occupational injury surveillance by using a severity threshold: development of a new occupational health indicator
Bibliographic record
Abstract
BACKGROUND: Hospital discharge data are used for occupational injury surveillance, but observed hospitalisation trends are affected by trends in healthcare practices and workers' compensation coverage that may increasingly impair ascertainment of minor injuries relative to severe injuries. The objectives of this study were to (1) describe the development of a severe injury definition for surveillance purposes and (2) assess the impact of imposing a severity threshold on estimated occupational and non-occupational injury trends. METHODS: Three independent methods were used to estimate injury severity for the severe injury definition. 10 population-based hospital discharge databases were used to estimate trends (1998-2009), including the National Hospital Discharge Survey (NHDS) and State Inpatient Databases (SID) from the Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality. Negative binomial regression was used to model injury trends with and without severity restriction and to test trend divergence by severity. RESULTS: Trend estimates for occupational injuries were biased downwards in the absence of severity restriction, more so than for non-occupational injuries. Imposing a severity threshold resulted in a markedly different historical picture. CONCLUSIONS: Severity restriction can be used as an injury surveillance methodology to increase the accuracy of trend estimates, which can then be used by occupational health researchers, practitioners and policy-makers to identify prevention opportunities and to support state and national investments in occupational injury prevention efforts. The newly adopted state-based occupational health indicator, 'Work-Related Severe Traumatic Injury Hospitalizations', incorporates a severity threshold that will reduce temporal ascertainment threats to accurate trend estimates.
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How this classification was reachedexpand
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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".