Prevalence and predictors of MRSA, ESBL, and VRE colonization in the ambulatory IBD population
Bibliographic record
Abstract
BACKGROUND AND AIMS: Inflammatory bowel disease (IBD) patients may be at increased risk of acquiring antibiotic-resistant organisms (ARO). We sought to determine the prevalence of colonization of methicillin-resistant Staphylococcus aureus (MRSA), Enterobacteriaceae containing extended spectrum beta-lactamases (ESBL), and vancomycin-resistant enterococi (VRE) among ambulatory IBD patients. METHODS: We recruited consecutive IBD patients from clinics (n=306) and 3 groups of non-IBD controls from our colon cancer screening program (n=67), the family medicine clinic (n=190); and the emergency department (n=428) from the same medical center in Toronto. We obtained nasal and rectal swabs for MRSA, ESBL, and VRE and ascertained risk factors for colonization. RESULTS: Compared to non-IBD controls, IBD patients had similar prevalence of colonization with MRSA (1.5% vs. 1.6%), VRE (0% vs. 0%), and ESBL (9.0 vs. 11.1%). Antibiotic use in the prior 3 months was a risk factor for MRSA (OR, 3.07; 95% CI: 1.10-8.54), particularly metronidazole. Moreover, gastric acid suppression was associated with increased risk of MRSA colonization (adjusted OR, 7.12; 95% CI: 1.07-47.4). Predictive risk factors for ESBL included hospitalization in the past 12 months (OR, 2.04, 95% CI: 1.05-3.95); treatment with antibiotics it the past 3 months (OR, 2.66; 95% CI: 1.37-5.18), particularly prior treatment with vancomycin or cephalosporins. CONCLUSIONS: Ambulatory IBD patients have similar prevalence of MRSA, ESBL and VRE compared to non-IBD controls. This finding suggests that the increased MRSA and VRE prevalence observed in hospitalized IBD patients is acquired in-hospital rather than in the outpatient setting.
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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) | 0.001 | 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".