Exposure to procedural ionizing radiation and cancer risk among physicians
Bibliographic record
Abstract
BACKGROUND: Physicians in certain specialities are routinely exposed to procedural ionizing radiation. Their risk of cancer is unknown, including by cancer sub-types. AIMS: To assess cancer risk among exposed physicians. METHODS: This population-based case-control study was completed in Ontario, Canada, where healthcare is universal, using linkage of physician billing claims to a province-wide cancer registry. Up to five cancer-free physician controls were matched to each cancer-affected physician, by sex, and both age at and year of, entry into practice. Cumulative exposure to procedural ionizing radiation was captured by physician billing claims. Conditional logistic regression generated an odds ratio (OR) of cancer per 1000 procedures performed and as a binary exposure comparing physicians above the upper 95th percentile cumulative number of procedures (≥200) to those below this cut point. RESULTS: Mean (standard deviation) age of the 1265 cases and 5772 non-cancer controls was 39.7 (10.7) and 37.7 (9.0) years, and 45% and 49% were female, respectively. After a median (interquartile ranges) of 13.0 (6.9-20.4) and 12.5 (6.5-20.1) years of lookback among cases and controls, the OR of cancer was 1.02 (95% confidence interval 0.99-1.05; P = NS) per 1000 additional procedures performed. Modelling the cumulative exposure to procedures nonlinearly did not change the observed association (P > 0.40 for each). Comparing physicians above versus below the upper 95th percentile cumulative number of procedures, the OR of cancer was 1.23 (95% confidence interval 0.75-2.01, P = NS). CONCLUSIONS: Physician exposure to procedural ionizing radiation was not associated with a higher risk of cancer. Measures that minimize radiation exposure should continue.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".