State Trauma Registries as a Resource for Occupational Injury Surveillance and Research
Bibliographic record
Abstract
OBJECTIVES: Work-related traumatic injury is a leading cause of death and disability among US workers. Occupational injury surveillance is necessary for effective prevention planning and assessing progress toward Healthy People 2020 objectives. Our objectives were to (1) describe the Washington State Trauma Registry (WTR) as a resource for occupational injury surveillance and research, (2) compare the WTR with 2 population-based data sources more widely used for these purposes, and (3) compare the number of injuries ascertained by the WTR with other data sources. METHODS: We linked WTR records to hospital discharge records in the Comprehensive Hospital Abstract Reporting System for 2009 and to workers' compensation claims from the Washington State Department of Labor and Industries for 1998 to 2008. We assessed the 3 data sources for overlap, concordance, and case ascertainment. RESULTS: Of 9185 work-related injuries in the WTR, 3380 (37%) did not link to workers' compensation claims. Use of payer information in hospital discharge records along with the WTR work-relatedness field identified 20% more linked injuries as work related (n = 720) than did use of payer information alone (n = 602). The WTR identified substantial numbers of work-related injuries that were not identified through workers' compensation or hospital discharge records. CONCLUSIONS: Workers' compensation and hospital discharge databases are important but incomplete data sources for work-related injuries; many work-related injuries are not billed to, reported to, or covered by workers' compensation. Trauma registries are well positioned to capture severe work-related injuries and should be included in comprehensive injury surveillance efforts.
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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 | 0.037 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.028 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".