Risk Factors for Community-associated Clostridium difficile-associated Diarrhea in Children
Bibliographic record
Abstract
BACKGROUND: Clostridium difficile-associated diarrhea (CDAD) is increasingly diagnosed in children in community settings. This study aims to assess recent antibiotic use and other risk factors in children with community-associated (CA-) CDAD compared with children with other diarrheal illnesses in a tertiary care setting. METHODS: Children with CA-CDAD evaluated at Texas Children's Hospital (Houston, TX) from January 1, 2012 to June 30, 2013 were identified. Two control subjects with community-associated diarrhea who tested negative for C. difficile were matched to case subjects. Data on demographics, medication exposure and outpatient healthcare encounters were collected from medical records. Multivariate logistic regression was performed to identify predictors of pediatric CA-CDAD. RESULTS: Of 69 CA-CDAD cases, most (62.3%) had an underlying chronic medical condition and 40.6% had antibiotic exposure within 30 days of illness. However, no traditional risk factor for CDAD was identified in 23.2% and 15.9% of CA-CDAD cases within 30 and 90 days of illness onset, respectively. Outpatient healthcare encounters within 30 days were more common among CA-CDAD cases than control subjects (66.7% vs. 48.6%; P = 0.01). In the final multivariate model, CA-CDAD was associated with cephalosporin use within 30 days [odds ratio: 3.32; 95% confidence interval: 1.10-10.01] and the presence of a gastrointestinal feeding device (odds ratio: 2.59; 95% confidence interval: 1.07-6.30). CONCLUSIONS: Recent use of cephalosporins and the presence of gastrointestinal feeding devices are important risk factors for community- associated CDAD in children. Reduction in the use of outpatient antibiotics may decrease the burden of CA-CDAD in children.
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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 | 0.001 |
| 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".