Understanding the Associations of Urogenital Microbiomes With Fertility and In Vitro Fertilization
Bibliographic record
Abstract
Infertility, defined as the inability to establish a clinical pregnancy after a year of regular, unprotected sexual intercourse, impacts 8%-12% of couples worldwide. Many of these couples turn to in vitro fertilization (IVF) to build their families. The success rate of IVF procedures is variable, with estimates of up to 40% of embryo transfers being unsuccessful. Herein we review the existing literature on the role of the female and male urogenital microbiomes and genital inflammation on fertility and IVF outcomes. We discuss the microbiome across the female reproductive tract (FRT) and identify associations with female infertility, female genital tract inflammation, and success of IVF procedures. We also discuss the male urogenital microbiome and the associations between microbial taxa, genital inflammation, and male fertility parameters. Finally, we consider microbial transfer within couples and the impact this may have on fertility and the success of IVF procedures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".