Urinary Tract Infections in Extended Care Facilities: Preventive Management Strategies
Bibliographic record
Abstract
OBJECTIVE: To provide health care professionals with an overview of interventions that may be done to reduce the incidence of urinary tract infections (UTIs) in elderly patients, especially those residing in extended care facilities. DATA SOURCES: A Medline search of the English literature was performed from 1980 to January 2006 to find literature relevant to urinary tract prophylaxis. Further references were hand-searched from relevant sources. STUDY SELECTION: When assessing the effectiveness of various clinical interventions for reducing the incidence of UTIs in the elderly, preference was given to more recent, double-blind, placebo-controlled randomized studies, but studies of less robust design also were included in the discussions when the former were lacking. DATA EXTRACTION: Where possible, recent publications were favored over older studies. References were all reviewed by the authors and chosen to present key citations. DATA SYNTHESIS: Data selection was prioritized to address specific subtopics. CONCLUSION: Though still frequent in occurrence and quite costly in terms of morbidity, mortality, and cost to the health care system, numerous measures may be taken to ameliorate the incidence of UTIs in elderly, institutionalized residents. First and foremost, establishing and adhering to good infection-control practices by health care givers and minimizing the use of indwelling catheters are essential. Adequate staffing and training are germane to this effort. Reasonably well-designed clinical studies also give credence to the use of topical estrogens and lactobacillus "probiotics" for female subgroups and cranberry juice for a wider array of patients. Vitamin C is of no proven benefit. With regard to antibiotics, with the relative paucity of data available for this patient population, concerns for resistance proliferation must be balanced against perceived gains in UTI reduction.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".