Risk Factors for Resistance to Antimicrobial Agents among Nursing Home Residents
Why this work is in the frame
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Bibliographic record
Abstract
The authors prospectively collected data on exposure to antimicrobial agents and susceptibility patterns among all clinical isolates of bacteria taken from 9,156 residents of 50 nursing homes in Canada and the United States in 1998-1999. Exposure to antimicrobial agents was measured during the 10 weeks prior to detection of targeted resistant bacteria in residents and compared with antibiotic exposure during a 10-week interval in individuals with sensitive organisms. These main effects were adjusted for infection-control and staffing covariates using multiple logistic regression modeling. Increased staffing of nursing homes with registered nurses (adjusted odds ratio (OR) = 0.79 (95% confidence interval (CI): 0.72, 0.87) per registered nurse per 100 resident-days) and use of antibacterial soap (adjusted OR = 0.40, 95% CI: 0.18, 0.90) were associated with reduced risk of methicillin-resistant Staphylococcus aureus in nursing home residents. An increase in the number of hand-washing sinks per 100 residents was shown to reduce the risk of trimethoprim-sulfamethoxazole (TMP/SMX)-resistant Enterobacteriaceae (adjusted OR = 0.94, 95% CI: 0.90, 0.98). Exposure to TMP-SMX and exposure to fluoroquinolones were significant risk factors for isolation of TMP-SMX-resistant Enterobacteriaciae (adjusted OR = 1.14, 95% CI: 1.06, 1.22) and fluoroquinolone-resistant Enterobacteriaciae (adjusted OR = 1.08, 95% CI: 1.04, 1.11), respectively. These findings suggest that increased staffing, more hand-washing sinks, and use of antimicrobial soap may reduce resistance to antimicrobial agents in long-term care facilities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it