Effect of a Fermented Milk Combining<i>Lactobacillus Acidophilus</i>CL1285 and<i>Lactobacillus Casei</i>in the Prevention of Antibiotic-Associated Diarrhea: A Randomized, Double-Blind, Placebo-Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Antibiotic-associated diarrhea is an important problem in hospitalized patients. The use of probiotics is gaining interest in the scientific community as a potential measure to prevent this complication. The main objective of the present study was to assess the efficacy and safety of a fermented milk combining Lactobacillus acidophilus and Lactobacillus casei that is widely available in Canada, in the prevention of antibiotic-associated diarrhea. METHODS: In this double-blind, randomized study, hospitalized patients were randomly assigned to receive either a lactobacilli-fermented milk or a placebo on a daily basis. RESULTS: Among 89 randomized patients, antibiotic-associated diarrhea occurred in seven of 44 patients (15.9%) in the lactobacilli group and in 16 of 45 patients (35.6%) in the placebo group (OR 0.34, 95% CI 0.125 to 0.944; P=0.05). The median hospitalization duration was eight days in the lactobacilli group, compared with 10 days in the placebo group (P=0.09). Overall, the lactobacilli-fermented milk was well tolerated. CONCLUSION: The daily administration of a lactobacilli-fermented milk was safe and effective in the prevention of antibiotic-associated diarrhea in hospitalized patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".