Is There Evidence to Support Probiotic Use for Healthy People?
Bibliographic record
Abstract
Probiotics are typically marketed as foods and dietary supplements, categories for products intended to maintain health in generally healthy populations and which, unlike drugs, cannot claim to treat or cure disease. This review addresses the existing evidence that probiotics are beneficial to healthy people. Our approach was to perform a descriptive review of efficacy evidence that probiotics can prevent urinary, vaginal, gastrointestinal, and respiratory infections, and improve risk factors associated with cardiovascular health or reduce antibiotic use. Other endpoints such as mental, dental, or immune health were not specifically addressed. We concluded that there is sufficient evidence of efficacy and safety for clinicians and consumers to consider using specific probiotics for some indications - such as the use of probiotics to support gut function during antibiotic use or to reduce the risk of respiratory tract infections - for certain people. However, we did not find a sufficiently high level of evidence to support unconditional, population-wide recommendations for other preventive endpoints we reviewed for healthy people. Although evidence for some indications is suggestive of the preventive benefits of probiotics, additional research is needed.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".