Prevalence and Incidence of Antimicrobial‐Resistant Organisms among Hospitalized Inflammatory Bowel Disease Patients
Bibliographic record
Abstract
BACKGROUND: Patients with inflammatory bowel disease (IBD) experience frequent hospitalizations and use of immunosuppressive medications, which may predispose them to colonization with antimicrobial-resistant organisms (ARO). OBJECTIVE: To determine the prevalence of ARO colonization on admission to hospital and the incidence of infection during hospitalization among hospitalized IBD patients. METHODS: A chart review comparing the prevalence of colonization and incidence of infection with methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci and extended-spectrum beta-lactamase-producing Enterobacteriaceae (ESBL) in hospitalized IBD patients with those of non-IBD controls was performed. RESULTS: On admission, there were no significant differences between IBD inpatients and controls in the prevalence of colonization of methicillin-resistant S aureus (1.0% versus 1.2%; P=0.74), vancomycin-resistant enterococci (0.2% versus 0%; P=1.0) or ESBL (4.1% versus 5.5%; P=0.33). Pooling data from historical clinic-based cohorts, IBD patients were more likely than controls to have ESBL colonization (19% versus 6.6%; P<0.05). Antibiotic use on admission was associated with ESBL colonization among IBD inpatients (OR 4.2 [95% CI 1.4 to 12.6]). The incidence of ARO infections during hospitalization was not significantly different between IBD patients and controls. Among IBD patients who acquired ARO infections during hospitalizations, the mean time interval from admission to infection was shorter for those who were already colonized with ARO on admission. CONCLUSIONS: This particular population of hospitalized IBD patients was not shown to have a higher prevalence or incidence of ARO colonization or infection compared with non-IBD inpatients.
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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".