Differences of the Fecal Microflora With Clostridium difficile Therapies
Bibliographic record
Abstract
BACKGROUND: During treatment of Clostridium difficile infection (CDI), patterns of pathogen reduction in relationship to changes in components of the normal microbiota are hypothesized to be predictive of response to treatment and subsequent sustained cure. METHODS: At a single center, subjects enrolled into phase 2 and 3 C. difficile treatment clinical trials (2003-2008) provided fecal samples to assess killing of C. difficile and changes to components of the microbiome. Quantitative bacterial cultures, measurement of C. difficile toxin titers, quantitative polymerase chain reaction of fecal samples for Bacteroidetes, Clostridium clusters XIVa and IV, and C. difficile were performed. RESULTS: Quantitative bacterial cultures showed a mean log10 C. difficile count (colony-forming units [CFU]) of 6.7 ± 2.0 at study entry; vancomycin treatment consistently reduced C. difficile counts to the limit of detection (2.0 log10 CFU/g), whereas metronidazole was associated with mean C. difficile counts 1.5-2 log10 higher at 10 days of treatment. In patients receiving tolevamer, C. difficile persisted in high counts during treatment; response to treatment was correlated with neutralization of toxin along with persistence of normal microbiota components. However, this was achieved in approximately half of subjects. Both vancomycin and metronidazole further suppressed microbiome components during treatment of CDI. Lactobacilli were observed to be a microbiome component that persisted during treatment of CDI. CONCLUSIONS: Differences of pathogen clearance and microbiome perturbation during treatment of CDI appear to explain treatment outcomes. The hypothesis that probiotic microbes could help prevent onset of CDI is supported by the observation of persistence of lactobacilli during and after treatment of CDI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".