The Relationship between Female Genital Mutilation and Infertility: <i>A systematic review and meta-analysis</i>
Bibliographic record
Abstract
This systematic review aimed to investigate the relationship between female genital mutilation (FGM) and infertility. Online databases were systematically searched up to January 2024 using MeSH keywords to retrieve relevant observational studies. The methodological quality of the analytical cross-sectional studies was assessed using the Newcastle–Ottawa Scale. Pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated, and a random-effects meta-analysis was used to address any heterogeneity. Additionally, a sensitivity analysis was performed. A total of 5 analytical cross-sectional studies involving 37,146 participants, with 3 studies meeting the criteria for the meta-analysis. The results indicated that female circumcision (FC) was linked to a 21% increase in the odds of developing infertility compared to non-circumcision, although this finding was not statistically significant (OR = 1.21, 95% CI: 0.98–1.50). Notably, there was no evidence of significant heterogeneity between the studies (P = 0.84 [Q statistics], I2 = 0.0%). While a statistically significant relationship between FGM and infertility was not established, the odds of infertility were higher in the circumcised group. Consequently, it is imperative to prioritise efforts to eradicate FC, especially among young girls.
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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".