The impact of maternal oral microbiota on the risk of small vulnerable newborns: a nested case-control study
Bibliographic record
Abstract
BACKGROUND: Small vulnerable newborns (SVNs) account for most neonatal deaths worldwide. Though maternal periodontal disease has been shown associated with an increased risk of preterm birth (PTB) and low birth weight (LBW), little evidence shows the potential mechanism. Our study aimed to explore the association between maternal oral microbiota and SVNs before and during pregnancy. METHODS: A nested 1:4 case-control study was undertaken. Women delivering SVNs, including spontaneous PTB, LBW, and small-for-gestational-age (SGA) newborns, were selected as cases, while women delivering normal newborns were randomly selected as controls. 480 unstimulated saliva samples were collected from 240 women (48 cases and 192 controls) in preconception and late pregnancy. 16 S rRNA gene sequencing was used for analysis. RESULTS: Women with SVNs showed lower richness index (p = 0.032) in oral microbiota during preconception, lower shannon (p = 0.028) and simpson (p = 0.023) index in late pregnancy compared to the control group. Granulicatella and Streptococcus were significantly enriched in saliva both before and during pregnancy in women delivering SVNs. The two evaluated genera were positively correlated with enriched metabolic pathways like lactose and galactose degradation. These genera and their species were also enriched among women in the PTB and SGA sub-groups. CONCLUSIONS: Women with SVNs exhibited significantly lower diversity in oral microbiota, with two enriched genera Granulicatella and Streptococcus in both before and during pregnancy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".