High Diversity and Variability in the Vaginal Microbiome in Women following Preterm Premature Rupture of Membranes (PPROM): A Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To characterize the vaginal microbiota of women following preterm premature rupture of membranes (PPROM), and determine if microbiome composition predicts latency duration and perinatal outcomes. DESIGN: A prospective cohort study. SETTING: Canada. POPULATION: Women with PPROM between 24+0 and 33+6 weeks gestational age (GA). METHODS: Microbiome profiles, based on pyrosequencing of the cpn60 universal target, were generated from vaginal samples at time of presentation with PPROM, weekly thereafter, and at delivery. MAIN OUTCOME MEASURES: Vaginal microbiome composition, latency duration, gestational age at delivery, perinatal outcomes. RESULTS: Microbiome profiles were generated from 70 samples from 36 women. Mean GA at PPROM was 28.8 wk (mean latency 2.7 wk). Microbiome profiles were highly diverse but sequences representing Megasphaera type 1 and Prevotella spp. were detected in all vaginal samples. Only 13/70 samples were dominated by Lactobacillus spp. Microbiome profiles at the time of membrane rupture did not cluster by gestational age at PPROM, latency duration, presence of chorioamnionitis or by infant outcomes. Mycoplasma and/or Ureaplasma were detected by PCR in 81% (29/36) of women, and these women had significantly lower GA at delivery and correspondingly lower birth weight infants than Mycoplasma and/or Ureaplasma negative women. CONCLUSION: Women with PPROM had mixed, abnormal vaginal microbiota but the microbiome profile at PPROM did not correlate with latency duration. Prevotella spp. and Megasphaera type I were ubiquitous. The presence of Mollicutes in the vaginal microbiome was associated with lower GA at delivery. The microbiome was remarkably unstable during the latency period.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".