Night shift work and breast cancer risk: what do the meta-analyses tell us?
Bibliographic record
Abstract
Objectives This paper aims to compare results, assess the quality, and discuss the implications of recently published meta-analyses of night shift work and breast cancer risk. Methods A comprehensive search was conducted for meta-analyses published from 2007-2017 that included at least one pooled effect size (ES) for breast cancer associated with any night shift work exposure metric and were accompanied by a systematic literature review. Pooled ES from each meta-analysis were ascertained with a focus on ever/never exposure associations. Assessments of heterogeneity and publication bias were also extracted. The AMSTAR 2 checklist was used to evaluate quality. Results Seven meta-analyses, published from 2013-2016, collectively included 30 cohort and case-control studies spanning 1996-2016. Five meta-analyses reported pooled ES for ever/never night shift work exposure; these ranged from 0.99 [95% confidence interval (CI) 0.95-1.03, N=10 cohort studies) to 1.40 (95% CI 1.13-1.73, N=9 high quality studies). Estimates for duration, frequency, and cumulative night shift work exposure were scant and mostly not statistically significant. Meta-analyses of cohort, Asian, and more fully-adjusted studies generally resulted in lower pooled ES than case-control, European, American, or minimally-adjusted studies. Most reported statistically significant between-study heterogeneity. Publication bias was not evident in any of the meta-analyses. Only one meta-analysis was strong in critical quality domains. Conclusions Fairly consistent elevated pooled ES were found for ever/never night shift work and breast cancer risk, but results for other shift work exposure metrics were inconclusive. Future evaluations of shift work should incorporate high quality meta-analyses that better appraise individual study quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.122 | 0.339 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.032 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".