Considerations of circadian impact for defining ‘shift work’ in cancer studies: IARC Working Group Report
Bibliographic record
Abstract
Based on the idea that electric light at night might account for a portion of the high and rising risk of breast cancer worldwide, it was predicted long ago that women working a non-day shift would be at higher risk compared with day-working women. This hypothesis has been extended more recently to prostate cancer. On the basis of limited human evidence and sufficient evidence in experimental animals, in 2007 the International Agency for Research on Cancer (IARC) classified 'shift work that involves circadian disruption' as a probable human carcinogen, group 2A. A limitation of the epidemiological studies carried out to date is in the definition of 'shift work.' IARC convened a workshop in April 2009 to consider how 'shift work' should be assessed and what domains of occupational history need to be quantified for more valid studies of shift work and cancer in the future. The working group identified several major domains of non-day shifts and shift schedules that should be captured in future studies: (1) shift system (start time of shift, number of hours per day, rotating or permanent, speed and direction of a rotating system, regular or irregular); (2) years on a particular non-day shift schedule (and cumulative exposure to the shift system over the subject's working life); and (3) shift intensity (time off between successive work days on the shift schedule). The group also recognised that for further domains to be identified, more research needs to be conducted on the impact of various shift schedules and routines on physiological and circadian rhythms of workers in real-world environments.
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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 | 0.391 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".