Clostridium difficile Colonization Before and After Hospitalization in Children
Bibliographic record
Abstract
BACKGROUND: Beginning in the early 2000s, Clostridium difficile infection has become a major health problem in the United States, Canada, and in most European countries and has not only increased in incidence but also the severity. There are 2 conditions for the development of C. difficile infection: disruption of the normal gastrointestinal flora, and exogenous ingestion of the microorganism. We aimed to study C. difficile colonization in hospitalized children. We identified 2 issues: (1) the relationship between risks before hospital admission and colonization on the first day of hospitalization and (2) the effect of the factors that patients are exposed to during hospitalization on the colonization status at discharge. METHODS: Patients aged between 2 and 18 years who were hospitalized with various diagnoses were included in this study. C. difficile toxin A/B was investigated in the stool samples taken on the admission and discharge days. RESULTS: One hundred six patients were included in the study, of whom 24.5% and 48.1% of hemato-oncology patients were positive for C. difficile toxin A/B. Antibiotic usage within 1 month preceding hospitalization and the presence of underlying disease impact the C. difficile colonization status on the first day of hospitalization. CONCLUSION: Toxigenic C. difficile colonization prevalence is high in hospitalized children, especially in the hemato-oncology patient group.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".