Sex/gender differences in the association between behavioural factors and cancers: an umbrella review of systematic reviews with quantitative synthesis
Bibliographic record
Abstract
Many systematic reviews have summarized evidence on the association between behavioural factors and incident cancers. To date, there has been little synthesis of heterogeneity by sex/gender of this evidence.An umbrella review was conducted of systematic reviews with quantitative synthesis (meta-analysis, meta-regression) examining the exposures of body size; physical activity; wholegrains, vegetables, fruit and beans; "fast foods"; red and processed meat; sugar sweetened drinks; dietary supplements; alcohol; tobacco; and sun exposure with incident non-sex-specific cancers. A search of Ovid MEDLINE, Ovid Embase, and Cochrane library from database inception to May 2023 was conducted. We calculated the proportion of systematic reviews that provided quantitative sex/gender findings (e.g., subgroup analyses) and summarized findings narratively. Methodological quality was appraised with the AMSTAR-2 tool.From 13,227 records, 705 full-text systematic reviews were identified as meeting inclusion criteria. Of these, 361 (51.2%) reported quantitative sex/gender findings. The terms "sex" and "gender" were used interchangeably by 36.3% of the 361 systematic reviews and none reported findings for transgender, gender-diverse, or non-binary individuals. Overall, 98.6% (356/361) of systematic reviews were rated "critically low" with the AMSTAR-2 tool. Most of the 361 systematic reviews with quantitative sex/gender findings reported no statistically significant differences by sex/gender.This umbrella review found conflation of sex and gender in systematic reviews of behavioural factors and non-sex-specific cancers and a lack of research among non-cisgender individuals. The existing evidence base is of critically low quality and our findings of no sex/gender-specific trends must be interpreted with caution.
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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 | 0.128 | 0.369 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.018 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".