Bibliographic record
Abstract
The authors reply: Although most breast cancers appear to be attributable to environmental exposures, 1 little is known about specific causes for this disease. Therefore, it is important to conduct epidemiologic studies in order to test all biological plausible hypotheses. It has been suggested that light-at-night may cause breast cancer and other hormone-related tumours. 2 Despite the high prevalence of persons who work at night and therefore are exposed to light-at-night, no major study has investigated this hypothesis. As an initial step to evaluate an association between night work and female breast cancer, we used a comprehensive data linkage for this purpose, in which it was possible to control for the major confounder, i.e. the reproductive outcome. 3 The strengths of our nationwide register linkage studies are their size and the lack of selection and information bias. 4 A major problem, however, is often the relatively imprecise available information on some exposures. In our attempt to classify workers with predominantly night work, we used the registered information of being employed over half a year in trades in which at least 60% of the women worked at night. Thereby we omitted a major group of hospital workers in which the proportion of night workers is 41%. Owing to our data linkage, the odds ratio for breast cancer among female Danish hospital workers is 1.2 (1.1–1.5), and among the major subgroup, the nurses, the OR is 1.3 (1.1–1.4). Thus, among these groups, which have a lower proportion of night workers than those in our recent study, 3 the increased relative risk of breast cancer further supports our hypothesis. As suggested by O’Connell and Buttimer, women employed in some trades with predominantly nighttime work may also be exposed to electromagnetic or cosmic radiation, which may also contribute to breast cancer risk in our study. Further, confounders such as alcohol, use of oral contraceptives or a lower level of physical activity among the night-time workers may at least partly have contributed to their observed increased risk of breast cancer, and should be considered in subsequent studies. Johnni Hansen
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".