Urinary Tract Infections in Long-term Care: Evaluation of Uropathogens, Antibiotic Susceptibility, and Empiric Treatment
Bibliographic record
Abstract
Objective To devise a residential empiric treatment algorithm, describe common uropathogens associated with urinary tract infections (UTIs) in residential care, assess all-pathogen and non-ESBL (extended-spectrum beta-lactamase) Escherichia coli antibiotic susceptibilities, and report the percentage of antibiotic use. Design A retrospective chart review of 198 residents with positive urine cultures from September 2019 to September 2020. Setting Institutional long-term care facility. Participants The exclusion criteria were negative urine culture, mixed organisms on urine culture, no antibiotic treatment, signs and symptoms of systemic infection, hospitalization because of systemic infection, and intravenous antibiotic treatment. The entire population was screened. Results The most prevalent pathogens were non-ESBL E. coli (29%), Proteus mirabilis (12%), Klebsiella pneumoniae (8%), and ESBL E. coli (8%). All-pathogen susceptibilities were 79.6% (amoxicillin/clavulanate), 64.1% (nitrofurantoin), 50.5% (sulfamethoxazole/trimethoprim), 43.7% (cephalexin), 42.7% (amoxicillin), and 41.8% (ciprofloxacin). Amoxicillin/clavulanate (96.7%), nitrofurantoin (90.0%) and sulfamethoxazole/trimethoprim (83.3%) demonstrated the highest non-ESBL E. coli susceptibilities. Nitrofurantoin was the most prescribed antibiotic (21%), followed by amoxicillin/clavulanate (19%) and ciprofloxacin (17%). Conclusion Based on the data, amoxicillin/clavulanate and nitrofurantoin are appropriate first-line options for empiric treatment of symptomatic cystitis in this long-term care facility, with sulfamethoxazole/trimethoprim as an alternative. Ciprofloxacin was overprescribed despite its low susceptibilities to commonly encountered pathogens, which emphasizes the need for a UTI empiric treatment algorithm tailored towards residential care.
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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 | 0.007 |
| 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 | 0.001 |
| 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".