Does probiotic ingestion reduce the risk of preeclampsia? A systematic review
Bibliographic record
Abstract
We aimed to systematically review the literature on the effects of probiotic consumption on the risk of preeclampsia (PE) development. Eight databases, clinical trial registries, and grey literature were searched until February 2022. Studies were included if they (1) were randomized clinical trials (RCTs), (2) included pregnant women aged ≥ 18 years old, (3) used probiotics products, and (4) were written in the Latin alphabet. A random-effects meta-analysis was performed using the risk ratio as the effect measure with 95% confidence intervals (CI) for PE. The search strategy identified 359 records, from which six RCTs were included. The six RCTs evaluated pregnant women with comorbidities and enrolled 593 women that received probiotics and 625 receiving placebo. None of the included RCTs analyzed healthy women. Probiotics increased by 12% the PE risk (RR 1.12, 95% CI, CI = 0.83–1.53, p = 0.46, χ2 = 3.31, df = 5 ( p = 0.65), I2 = 0%). The certainty of the evidence, evaluated through the Grading of Recommendations Assessment, Development and Evaluation approach, was rated as very low. In conclusion, probiotics supplementation may slightly increase PE rates in pregnant women with comorbidities. The risk may be higher in obese women and for periods of ingestion longer than eight weeks. However, the evidence certainty is very low. PROSPERO registration No.CRD42021278611.
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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".