Estimating Nurses' Exposures to Ionizing Radiation: The Elusive Gold Standard
Bibliographic record
Abstract
This study assessed ionizing radiation exposure in 58,125 registered nurses in British Columbia, Canada, for a cohort study of cancer morbidity and mortality. Two methods were used: (1) a survey of nurses in more than 100 acute care hospitals and health care centers; (2) and monitoring data reported to the National Dose Registry of Health Canada, considered the gold standard. The mean exposure of cohort nurses monitored during the study period from 1974 to 2000 was 0.27 milliSieverts (7028 person-years of monitoring). Of 609,809 person-years in the cohort, 554,595 (90.9%) were identified as unexposed by both exposure assessment methods. Despite crude agreement of 91% between the methods, weighted kappa for agreement beyond chance was only 0.045, and the sensitivity of the survey method to capture National Dose Registry monitored person-years was only 0.085 (specificity = 0.97). The survey missed exposures outside the acute care setting. The National Dose Registry also missed potential exposures, especially among hospital emergency department and pediatric staff nurses. It was unlikely that either method estimated nurses' true exposures to ionizing radiation with good sensitivity and specificity. The difficulty in exposure assessment likely arises because fewer than 10% of registered nurses are exposed to ionizing radiation, yet the settings in which they are exposed vary tremendously. This means that careful hazard assessment is required to ensure that monitoring is complete where exposures are probable, without incurring the excess costs and lack of specificity of including the unexposed.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".