Studies in Cancer Epigenetics through a Sex and Gendered Lens: A Comprehensive Scoping Review
Bibliographic record
Abstract
Background: Sex and gender are vitally important in the study of epigenetic mechanisms for various types of cancer. However, little has been done to assess the state of sex and gender-based analyses (SGBA) in this field. The aim was to undertake a critical evaluation of sex and gender representation, discussion, and data analysis within the cancer epigenetics field since 2010. Methods: A PRISMA-ScR scoping review was conducted with 111 peer-reviewed studies comprising of colorectal, gastric, head and neck, hepatocellular carcinoma, and lung cancers. Data extraction and a quality appraisal were performed by a team of epidemiologists and bioethicists. Results: Of the 111 included studies, only 17 studies (15.3%) explicitly stated sex and gender analysis to be their primary aim. A total of 103 studies (92.8%) provided a detailed analysis of sex/gender as a biological or social variable, while the remaining 8 studies (7.2%) only stratified results by sex/gender. Although sex and gender were a key facet in all the eligible studies, only 7 studies (6.3%) provided an explicit definition of the terms “sex” or “gender”, while the remaining 104 studies (93.7%) used the words “sex” or “gender” without providing a definition. A total of 84 studies (75.7%) conflated the concepts of “sex” and “gender”, while 44 studies (39.6%) were inconsistent with their usage of the “sex” and “gender” terms. Conclusions: Very few studies offered a robust analysis of sex/gender data according to SAGER guidelines. We call for clear and directed guidelines regarding the use of sex/gender as a variable in epigenetics research.
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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 | 0.052 | 0.182 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.032 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".