Linkage and Concordance of Trauma Registry and Hospital Discharge Records
Bibliographic record
Abstract
OBJECTIVE: Occupational injury researchers typically use payer to identify work-related injuries in hospital discharge records. Many trauma registries contain a work-related field, independent of payer. Linked trauma registry and hospital discharge records were used to assess data field concordance and to assess the validity of using payer or external cause of injury (E-codes) to identify work-related injuries. METHODS: Washington State Trauma Registry records were linked to hospital discharges (year 2009). RESULTS: There was substantial agreement between Washington State Trauma Registry and hospital discharge records for workers' compensation as primary payer. E-code based methods of identifying occupational injuries had high specificity (more than 99%) but low sensitivity (less than 14%). Payer was 76% sensitive and 98% specific. CONCLUSIONS: This study found substantial agreement for data fields key to occupational injury surveillance and research. Nevertheless, many work-related injuries could not be identified using hospital discharge records.
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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 | 0.054 | 0.192 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".