0282 Quantifying the impact of shift work on breast cancer: results from the burden of occupational cancer in canada study
Bibliographic record
Abstract
Objectives To estimate the proportion and number of annual incident female breast cancer cases in Canada attributed to shift work, a probable carcinogen. Methods Levin’s equation, which contains exposure and relative risk (RR) parameters, was used to calculate an attributable fraction (AF) range. The proportion of Canadian women who ever worked night or rotating shifts between 1961 and 2001 was retrospectively assessed based on data from the 1996 Survey of Labour and Income Dynamics. Low and high RR values, selected from a comprehensive review and quality assessment of recently published meta-analyses, were used to represent the probable association between shift work and breast cancer risk. The AF range calculated from these data were applied to 2011 Canadian breast cancer incidence statistics to obtain the number of attributable cases. Results Approximately 11%, or 1.5 million, Canadian women ever worked night or rotating shifts during 1961–2001. Combined with low and high RR values of 1.15 and 1.40 from a high-quality meta-analysis published in 2013, the AF for breast cancer ranged from 2.04% to 5.23%. This corresponds to an estimated 460–1180 newly diagnosed breast cancers each year in Canada probably due to shift work. A large number, approximately 200–510, of these cancers occur among women in the health care and social assistance sector. Conclusions The burden of occupational breast cancer in Canada could be substantial, reflecting the high prevalence of shift work and incidence of breast cancer. Although more research is needed on unravelling this probable association, preventive approaches should be widely considered.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".