Correlation of Clinical Severity With Stool Microbiome Changes in <i>Clostridioides difficile</i> Infection
Bibliographic record
Abstract
CONTEXT.—: Clostridioides difficile infection (CDI) is the world-leading cause of infectious nosocomial diarrhea and pseudomembranous colitis. Antibiotics are the first line of treatment against CDI despite the high likelihood of antibiotic failure and/or recurrence. More data are needed to correlate clinical variables with 16S rRNA microbiome profiles in CDI-infected patients. OBJECTIVE.—: To determine the relationship(s) between a patient's clinical factors and the stool bacteriome of CDI-positive patients and CDI-negative patients with diarrheal symptoms. DESIGN.—: This study used stool samples and clinical data from 358 patients with nosocomial diarrhea, who were divided by their CDI diagnosis (CDI-negative: n = 180; CDI-positive; n = 178). The stool bacteriome was profiled by amplicon deep sequencing of the 16S rRNA gene, followed by correlating clinical data. RESULTS.—: The stool bacteriome was significantly different by severity assessment regardless of CDI status. Phyla and species varied significantly by CDI diagnosis. Severity, defined as a serum white blood cell count greater than 15 cells/μL and/or a creatinine level greater than 1.5 mg/dL, correlated significantly with dysbiosis of the stool bacteriome profile of CDI-positive patients compared to CDI-negative patients. Serum white blood cell count was significantly higher in patients with bacterial dysbiosis, and high levels of creatinine were associated with low bacteriome diversity. CONCLUSIONS.—: Clinical severity of CDI influences the stool microbiome of infected patients. To date, this study has the largest data set comparing 16S rRNA microbiome profiles and clinical variables between CDI-infected and noninfected individuals.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".