The Effect of Probiotics on Health in Pregnancy and Infants: A Randomized, Double-Blind, Placebo-Controlled Trial
Bibliographic record
Abstract
Background/Objectives: There is growing interest in the benefits of probiotic supplementation during pregnancy and lactation, but evidence supporting the beneficial effects for mother–infant dyads remains scarce. This study assessed the effects of probiotic supplementation on infection frequency and immunity in pregnant women and infants, and on microbiome establishment during the first month of life. Methods: At 28 weeks of gestation, 180 healthy pregnant women were randomized to receive either a placebo (n = 90) or a probiotic supplement (n = 90), Prenatis™, containing 5 billion CFU/day of Lacticaseibacillus rhamnosus Rosell®-11 and Bifidobacterium bifidum HA-132. Results: There was a significantly lower number of women with one or more infections during the study in the probiotics group (8 vs. 18, p = 0.05) and a trend for a lower number of infections during pregnancy (primary outcome) in the probiotics group (p = 0.07). Regarding infants, a lower number of days with infections during the first month of life was observed in the probiotics group (4.7 days on average vs. 10.5 days, p = 0.03). The vaginal microbiota composition during pregnancy and after childbirth showed no significant differences between groups while the infants’ gut microbiome demonstrated a significantly higher abundance/prevalence of beneficial taxa in the probiotics group. The benefits conferred by probiotics were especially notable when considering birth by C-section. Probiotics promoted the vertical transmission of beneficial species and the induction of a highly interconnected microbiota, structured around key species. Conclusions: Probiotic supplementation during the third trimester of pregnancy and lactation is a valid strategy for conferring benefits to mothers and infants.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".