Safety and efficacy of probiotic supplements as adjunctive therapies in patients with COVID-19: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Oral probiotic supplementation may be a beneficial adjunctive therapy for patients with symptomatic COVID-19. However, its safety and efficacy are unclear. We aimed to investigate how probiotic supplementation impacts COVID-19 symptom trajectory and patient outcomes by conducting a systematic review and meta-analysis of randomized controlled trials (RCTs). METHODS: RCTs randomizing patients with COVID-19 to probiotics were searched in PubMed Central, Embase, CINAHL, and Cochrane Library from inception to July 31, 2022. We performed a random-effects pairwise meta-analysis for all outcomes using the restricted maximum likelihood (REML) estimator. We used the GRADE approach to assess the certainty of the evidence. RESULTS: A total of 1027 participants from eight RCT studies were included in the meta-analysis. Probiotic supplements probably reduce the incidence of diarrhea (RR 0.61 [0.43 to 0.87]; moderate certainty) and probably reduce cough or dyspnea compared to placebo/standard care (RR 0.37 [0.19 to 0.73]; moderate certainty). Probiotic supplements may improve composite endpoint measured by clinical escalation or mortality compared to placebo (RR 0.41 [0.18 to 0.93]; low certainty evidence); however, they may not significantly reduce the need for clinical escalation (RR 0.57 [0.31 to 1.07]; low certainty evidence) or mortality (RR 0.50 [0.20 to 1.29]; low certainty evidence). In addition, the probiotic supplement is associated with reduced adverse events (RR 0.62 [0.46 to 0.83]; moderate certainty). CONCLUSION: Early probiotic supplement is a safe and effective adjunctive therapy that reduces the risk of symptoms and health care burden related to COVID-19 across all severity types.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.047 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".