Systematic review and meta‐analysis on the use of probiotic supplementation in pregnant mother, breastfeeding mother and infant for the prevention of atopic dermatitis in children
Bibliographic record
Abstract
Probiotic supplementation may decrease the risk of allergic disease; however, there are differences between studies, such as the type of probiotic, the route or the duration of supplementation. Therefore, determining the most effective probiotic strain/s, route of administration and duration for clinical recommendation has been difficult. An electronic systematic literature search was undertaken between using Ovid MEDLINE, Embase, PubMed and Cochrane. Risk ratio (RR) and 95% confidence interval (CI) are presented for the studies. PEDro scale and Newcastle-Ottawa Scale were used to assess the quality of the included studies. A total of 21 studies met the inclusion criteria. Strain-specific sub-meta-analyses indicated that single strains are not as effective as probiotic mixtures and administration to a combination of pregnant mothers, breastfeeding mothers and infants had a reduced risk in the onset of atopic dermatitis in children. Our systematic review and meta-analysis showed that a mixture of probiotic supplementation given to the mother in pregnancy and continuing while breastfeeding and also to the infant in children classified as high-risk for atopic dermatitis and non-high-risk groups is the most efficacious in reducing the risk of incidence of atopic dermatitis in children.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".