An evaluation of hospital discharge records as a tool for serious work related injury surveillance
Bibliographic record
Abstract
OBJECTIVES: To identify and describe work related serious injuries among sawmill workers in British Columbia, Canada using hospital discharge records, and compare the agreement and capturing patterns of the work related indicators available in the hospital discharge records. METHODS: Hospital discharge records were extracted from 1989 to 1998 for a cohort of sawmill workers. Work related injuries were identified from these records using International Classification of Disease (ICD-9) external cause of injury codes, which have a fifth digit, and sometimes a fourth digit, indicating place of occurrence, and the responsibility of payment schedule, which identifies workers' compensation as being responsible for payment. RESULTS: The most frequent causes of work related hospitalisations were falls, machinery related, overexertion, struck against, cutting or piercing, and struck by falling objects. Almost all cases of machinery related, struck by falling object, and caught in or between injuries were found to be work related. Overall, there was good agreement between the two indicators (ICD-9 code and payment schedule) for identifying work relatedness of injury hospitalisations (kappa = 0.75, p < 0.01). There was better concordance between them for injuries, such as struck against, drowning/suffocation/foreign body, fire/flame/natural/environmental, and explosions/firearms/hot substance/electric current/radiation, and poor concordance for injuries, such as machinery related, struck by falling object, overexertion, cutting or piercing, and caught in or between. CONCLUSIONS: Hospital discharge records are collected for administrative reasons, and thus are readily available. Depending on the coding reliability and validity, hospital discharge records represent an alternative and independent source of information for serious work related injuries. The study findings support the use of hospital discharge records as a potential surveillance system for such injuries.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".