Primary prevention of Clostridium difficile infections with a specific probiotic combining Lactobacillus acidophilus, L. casei, and L. rhamnosus strains: assessing the evidence
Bibliographic record
Abstract
Clostridium difficile infection (CDI) has become the leading healthcare-associated infection and cause of outbreaks around the world. Although various innovative treatments have been developed, preventive strategies using multi-faceted infection control programmes have not been successful in reducing CDI rates. The major risk factor for CDI is the disruption of the normally protective gastrointestinal microbiota, typically by antibiotic use. Supplementation with specific probiotics has been effective in preventing various negative outcomes, including antibiotic-associated diarrhoea and CDI. However, a consensus of which probiotic strains might prevent CDI has not been reached and meta-analyses report high degrees of heterogeneity when studies of different probiotic products are pooled together. We searched the literature for probiotics with sufficient evidence to assess clinical efficacy for the prevention of CDI and focused on one specific probiotic formulation comprised of three lactobacilli strains (Lactobacillus acidophilus CL1285, Lactobacillus casei LBC80R, Lactobacillus rhamnosus CLR2, Bio-K+) for its ability to prevent CDI in healthcare settings. A literature search on this probiotic formulation was conducted using electronic databases (PubMed, Google Scholar), abstracts from infectious disease and infection control meetings, and communications from the probiotic company. Supporting evidence was found for its mechanisms of action against CDI and that it has an excellent safety and tolerability profile. Evidence from randomized controlled trials and facility-level interventions that administer Bio-K+ show reduced incidence rates of CDI. This probiotic formulation may have a role in primary prevention of healthcare-associated CDI when administered to patients who receive antibiotics.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".