Is primary prevention of Clostridium difficile infection possible with specific probiotics?
Bibliographic record
Abstract
BACKGROUND: The efficacy of probiotics for the prevention of Clostridium difficile infection (CDI) is highly controversial, particularly with regard to the prevention of recurrent CDI. We hypothesize that primary prevention of CDI among patients receiving antibiotics might be a more achievable goal for probiotics than prevention in patients with previous CDI where the host flora is markedly altered. METHODS: We conducted a literature search for randomized, placebo-controlled efficacy studies of probiotic use among adults receiving antibiotics, in which CDI was one of the outcomes measured. In addition, we conducted meta-analyses of probiotics that were included in more than one randomized trial. RESULTS: Eleven studies were identified; most were seriously underpowered to determine the efficacy of probiotics in the prevention of CDI. Two showed significantly lower rates of CDI among the probiotic recipients. A meta-analysis of three studies that used the probiotic combination Lactobacillus acidophilus CL1285 and Lactobacillus casei LBC80R and a combined analysis of those studies with four studies that used Saccharomyces boulardii, showed lower CDI rates in recipients of probiotics compared with recipients of placebo (risk ratio=0.39; 95% confidence interval 0.19-0.79). CONCLUSIONS: While potential flaws in study design were identified, a review of the available literature suggests that the primary prevention of CDI with specific probiotic agents may be achievable. Additional studies of sufficient size and with rigorous design are needed to confirm these findings.
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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 | 0.029 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".