Comparison of internal and external reference populations for occupational cancer surveillance in a cohort drawn from a diverse workforce
Bibliographic record
Abstract
OBJECTIVES: Prior analyses of the Occupational Disease Surveillance System (ODSS) have compared cancer rates using internal referent groups. As an exploratory analysis, we sought to estimate cancer risk using general population reference rates to evaluate the impact that the comparison population has on findings from our surveillance program. METHODS: A cohort of approximately 2.3 million workers in Ontario, Canada with an accepted lost-time workers' compensation claim were followed for all cancer diagnoses between 1983 and 2018. Standardized incidence ratios (SIRs) and 95% confidence intervals were calculated for workers in specific occupational groups using (1) all other workers in the ODSS cohort, and (2) the general population of Ontario. RESULTS: SIRs using the general population reference group were generally equal to or modestly lower compared to SIRs using the internal reference group. Within occupation groups, SIRs had a discordant direction of association (increased rate in the internal comparison and decreased in the external comparison) for some cancer sites including urinary, prostate, and colorectal. CONCLUSIONS: Findings emphasize the importance of the choice of reference group when evaluating cancer risks in large occupational surveillance cohorts. Importantly, the magnitude of confounding and the healthy worker hire bias may depend on the occupation group and cancer site of interest.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".