The vaginal microbiome of pregnant women is less rich and diverse, with lower prevalence of Mollicutes, compared to non-pregnant women
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The vaginal microbiome plays an important role in maternal and neonatal health. Imbalances in this microbiota (dysbiosis) during pregnancy are associated with negative reproductive outcomes, such as pregnancy loss and preterm birth, but the underlying mechanisms remain poorly understood. Consequently a comprehensive understanding of the baseline microbiome in healthy pregnancy is needed. We characterized the vaginal microbiomes of healthy pregnant women at 11-16 weeks of gestational age (n = 182) and compared them to those of non-pregnant women (n = 310). Profiles were created by pyrosequencing of the cpn60 universal target region. Microbiome profiles of pregnant women clustered into six Community State Types: I, II, III, IVC, IVD and V. Overall microbiome profiles could not be distinguished based on pregnancy status. However, the vaginal microbiomes of women with healthy ongoing pregnancies had lower richness and diversity, lower prevalence of Mycoplasma and Ureaplasma and higher bacterial load when compared to non-pregnant women. Lactobacillus abundance was also greater in the microbiomes of pregnant women with Lactobacillus-dominated CSTs in comparison with non-pregnant women. This study provides further information regarding characteristics of the vaginal microbiome of low-risk pregnant women, providing a baseline for forthcoming studies investigating the diagnostic potential of the microbiome for prediction of adverse pregnancy outcomes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it