Dataset from a Systematic Literature Review and Meta-Analysis of Genetic Factors in Endometriosis
Bibliographic record
Abstract
Description This dataset accompanies a systematic literature review (SLR) and meta-analysis examining the role of genetic factors in endometriosis. The central research question was: “To what degree is endometriosis a hereditary disease?” Systematic searches were conducted using the Medical Subject Headings (MeSH) terms: (“endometriosis”) AND (gene OR associated gene OR genetics OR hereditary OR inheritance). The dataset contains extracted study-level information, risk of bias assessments, and pooled results from included studies. It has been structured to promote transparency, reproducibility, and secondary analyses within the field of endometriosis genetics. Objective The dataset provides a curated and structured collection of studies examining genetic associations with endometriosis. By making this data openly available, it supports replication, data reuse, and further investigations into the genetic basis of endometriosis. Methods Search strategy: Searches were conducted in PubMed, Cochrane Library, Scopus, Medline, and CINAHL Ultimate up to March 20, 2025. Inclusion criteria: Studies involving reproductive-aged women (15–49 years) with surgically confirmed endometriosis and reporting genetic traits, gene mutations, gene expression, or inheritance patterns. Exclusion criteria: Non-human studies (animal or cell line); studies on adenomyosis or ovarian cancer; drug-related studies; those lacking genetic or inheritance components; case studies, reviews, or meta-analyses; articles without full-text access; publications in languages other than English; or studies with <10 participants or unreported sample size. Data extraction: Study design, population characteristics, genetic factors, and outcomes were extracted. Analysis: Where feasible, meta-analyses pooled effect sizes using a random-effects model. Supplementary Data Supplementary Data I: Protocol Supplementary Data II: Systematic Literature Review (one page per step of the process) Supplementary Data III: Newcastle-Ottawa Scale (NOS) assessment scoring Supplementary Data IV: Data Extraction and Analysis Citation If you use this dataset, please cite as: Sulayman, H. (2025) ‘Dataset from a Systematic Literature Review and Meta-Analysis of Genetic Factors in Endometriosis’. Zenodo. doi:10.5281/zenodo.17100919
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 | 0.014 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.022 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.139 | 0.009 |
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".