Patient- and hospital-level predictors of vancomycin-resistant Enterococcus (VRE) bacteremia in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Data are limited on risk factors for vancomycin-resistant Enterococcus (VRE) bacteremia. METHODS: All patients with a confirmed VRE bacteremia in Ontario, Canada, between January 2009 and December 2013 were linked to provincial healthcare administrative data sources and frequency matched to 3 controls based on age, sex, and aggregated diagnosis group. Associations between predictors and VRE bacteremia were estimated by generalized estimating equations and summarized using odds ratios (ORs) and corresponding 95% confidence intervals (CIs). RESULTS: In total, 217 cases and 651 controls were examined. In adjusted analyses, patient-level predictors included bone marrow transplant (OR 106.99 [95% CI 12.19-939.26]); solid organ transplant (OR 17.17 [95% CI 4.95-59.54]); any cancer (OR 8.64 [95% CI 3.88-19.21]); intensive care unit (ICU) admission (OR 6.81 [95% CI 3.53-13.13]); heart disease (OR 5.27 [95% CI 2.00-13.90]); and longer length of stay (OR 1.07 per day [95% CI 1.06-1.09]). Hospital-level predictors included hospital size (per increase in 100 beds (OR 1.26 [95% CI 1.07-1.48]) and teaching hospitals (OR 3.87 [95% CI 1.85-8.08]). CONCLUSIONS: Patients with a bone marrow transplant, solid organ transplant, cancer, or who are admitted to the ICU are at highest risk of VRE bacteremia, particularly at large hospitals and teaching hospitals.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".