Efficacy of Probiotics in Preventing Antibiotic-Associated Diarrhea in Outpatients
Bibliographic record
Abstract
Antibiotic-associated diarrhea (AAD) is a prevalent adverse influence of antibiotic management, particularly among outpatients receiving broad-spectrum or prolonged antibiotic courses. AAD can negatively impact treatment adherence, quality of life, and healthcare costs, and in some cases may progress to severe complications such as Clostridioides difficile infection. Probiotics were suggested as a preventive strategy to restore gut microbiota balance and decrease the frequency of AAD. However, variability in probiotic strains, dosages, and management regimens has led to inconsistent findings across studies, necessitating a comprehensive assessment of their efficiency and safety in outpatient settings. Methods: A systematic review has been performed utilizing EMBASE, Library, MEDLINE, Scopus, Cochrane and Web of Science. Searches employed keywords and MeSH terms including “antibiotic-associated diarrhea,” “AAD,” “probiotics,” “outpatients,” “Lactobacillus,” “Bifidobacterium,” and “Saccharomyces boulardii.” Eligible investigations involved randomized controlled trials (RCTs) and cohort investigations involving adult or pediatric outpatients receiving antibiotics with concurrent probiotic supplementation. Extracted data encompassed study design, probiotic strains and doses, incidence of AAD, duration and degree of diarrhea, and reported adverse events. Results: Five studies met the inclusion criteria. Probiotic supplementation was related to a significant decrease in the frequency of AAD in comparison with placebo or no intervention, with relative risk reductions ranging from 30% to 60%. Multistrain probiotics and Sacchar.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".