Pregnancy outcomes in women taking probiotics or prebiotics: a systematic review and meta-analysis
Bibliographic record
Abstract
Probiotics are living microorganisms that, when administered in adequate amounts, confer a health benefit. It has been speculated that probiotics might help prevent preterm birth, but in two previous systematic reviews possible major increases in this risk have been suggested. Our objective was to perform a systematic review and meta-analysis of the risk of preterm birth and other adverse pregnancy outcomes in pregnant women taking probiotics, prebiotics or synbiotics. We searched six electronic databases (MEDLINE, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, Web of Science’s Core collection and BIOSIS Preview) up to September 2016 and contacted authors for additional data. We included randomized controlled trials in which women with a singleton pregnancy received a probiotic, prebiotic or synbiotic intervention. Two independent reviewers extracted data using a piloted form and assessed the risk of bias using the Cochrane risk of bias tool. We used random-effects meta-analyses to pool the results. We identified 2574 publications, screened 1449 non-duplicate titles and abstracts and read 160 full text articles. The 49 publications that met our inclusion criteria represented 27 studies. No study used synbiotics, one used prebiotics and the rest used probiotics. Being randomized to take probiotics during pregnancy neither increased nor decreased the risk of preterm birth < 34 weeks (RR 1.03, 95% CI 0.29–3.64, I2 0%, 1017 women in 5 studies), preterm birth < 37 weeks (RR 1.08, 95% CI 0.71–1.63, I2 0%, 2484 women in 11 studies), or most of our secondary outcomes, including gestational diabetes mellitus. We found no evidence that taking probiotics or prebiotics during pregnancy either increases or decreases the risk of preterm birth or other infant and maternal adverse pregnancy outcomes. We prospectively published the protocol for this study in the PROSPERO database ( CRD42016048129 ).
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.046 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".