The Utility of Admission Screening for the Prevention of Nosocomial Transmission of Extended-spectrum β-Lactamase Producing Enterobacteriaceae
Bibliographic record
Abstract
Background: The efficacy of interventions to prevent in-hospital transmission of extended-spectrum β-lactamase-producing Enterobacteriaceae (ESBL-E) is poorly defined, particularly for admission screening. \nMethods: Variability in ESBL-E infection control practices was evaluated with a survey of 15 hospitals. All ESBL-E positive clinical and screening specimens at 12 hospitals (6 screening and 6 non-screening) from 2005-2009 were included and defined as hospital-onset or community-onset using standard definitions. ESBL-E incidence and susceptibility were studied. Screening efficacy was evaluated with a negative binomial model, adjusting for study year and incidence of community-onset cases. \nResults: Diverse practices in infection control for ESBL-E were found with 53.3% of hospitals utilizing admission screening. Overall incidence and hospital-onset cases increased 4-fold and 2-fold, respectively. Fluoroquinolone susceptibility for E. coli (12.8%) and K. pneumoniae (9.0%) was low. Hospital-onset cases were 49.1% lower in screening compared to non-screening hospitals (p<0.001). \nConclusion: Admission screening can reduce the incidence of hospital-onset ESBL-E cases.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".