Efficacy of Admission Screening for Extended-Spectrum Beta-Lactamase Producing Enterobacteriaceae
Bibliographic record
Abstract
OBJECTIVE: We hypothesized that admission screening for extended-spectrum β-lactamase-producing Enterobacteriaceae (ESBL-E) reduces the incidence of hospital-acquired ESBL-E clinical isolates. DESIGN: Retrospective cohort study. SETTING: 12 hospitals (6 screening and 6 non-screening) in Toronto, Canada. PATIENTS: All adult inpatients with an ESBL-E positive culture collected from 2005-2009. METHODS: Cases were defined as hospital-onset (HO) or community-onset (CO) if cultures were positive after or before 72 hours. Efficacy of screening in reducing HO-ESBL-E incidence was assessed with a negative binomial model adjusting for study year and CO-ESBL-E incidence. The accuracy of the HO-ESBL-E definition was assessed by re-classifying HO-ESBL-E cases as confirmed nosocomial (negative admission screen), probable nosocomial (no admission screen) or not nosocomial (positive admission screen) using data from the screening hospitals. RESULTS: There were 2,088 ESBL-E positive patients and incidence of ESBL-E rose from 0.11 to 0.42 per 1,000 inpatient days between 2005 and 2009. CO-ESBL-E incidence was similar at screening and non-screening hospitals but screening hospitals had a lower incidence of HO-ESBL-E in all years. In the negative binomial model, screening was associated with a 49.1% reduction in HO-ESBL-E (p<0.001). A similar reduction was seen in the incidence of HO-ESBL-E bacteremia. When HO-ESBL-E cases were re-classified based on their admission screen result, 46.5% were positive on admission, 32.5% were confirmed as nosocomial and 21.0% were probable nosocomial cases. CONCLUSIONS: Admission screening for ESBL-E is associated with a reduced incidence of HO-ESBL-E. Controlled, prospective studies of admission screening for ESBL-E should be a priority.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".