Effects of a Fermented Dairy Drink Containing Lacticaseibacillus paracasei subsp. paracasei CNCM I-1518 (Lactobacillus casei CNCM I-1518) and the Standard Yogurt Cultures on the Incidence, Duration, and Severity of Common Infectious Diseases: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
There is considerable interest in the role of probiotics in immune function. The objective of this systematic review and meta-analysis was to assess the effects of the consumption of a fermented dairy drink containing Lacticaseibacillus paracasei subsp. paracasei CNCM I-1518 (the previous taxonomic nomenclature was Lactobacillus casei CNCM I-1518, prior to the nomenclature change in April 2020) and the standard yogurt cultures (hereinafter referred to collectively as “FDD”) on common infectious diseases (CIDs) in generally healthy children and adults. Nine literature databases were searched, and nine randomized controlled trials from eight publications were eligible for inclusion. Combined effect sizes were determined for three metrics of CID incidence, two metrics of CID duration, and one metric of CID severity. Compared to the control, the consumption of the FDD resulted in (1) a significant reduction in the odds of experiencing ≥1 CID (odds ratio (OR) (with a 95% confidence interval (CI)): 0.81 (0.66, 0.98); p = 0.029); (2) a significant reduction in mean CIDs per subject (−0.09 (−0.15, −0.04); p = 0.001); and (3) a trend towards reduced risk in cumulative CIDs (relative risk (RR): 0.91 (0.82, 1.01); p = 0.082). The consumption of the FDD had no significant effect on CID duration or severity. Based on the studies conducted thus far, these results suggest that the FDD may reduce CID incidence in the general population.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".