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Record W2761208779 · doi:10.5489/cuaj.4472

Does cranberry have a role in catheter-associated urinary tract infections?

2017· article· en· W2761208779 on OpenAlexvenueno aff
Dominique Thomas, Matthew P. Rutman, Kimberly L. Cooper, Andrew Abrams, Julia B. Finkelstein, Bilal Chughtai

Bibliographic record

VenueCanadian Urological Association Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAntibioticsUrinary systemAdverse effectInternal medicineCRANBERRY JUICEAntibiotic resistanceUrinePlaceboMicrobiologyBiologyPathology

Abstract

fetched live from OpenAlex

Introduction: Catheter-associated urinary tract infections (CA-UTIs) are a prevalent and costly condition, with very few therapeutic options. We sought to evaluate the efficacy of an oral cranberry supplement on CA-UTIs over a six-month period. Methods: Subjects with long-term indwelling catheters and recurrent symptomatic CA-UTIs were enrolled to take a once-daily oral cranberry supplement with 36 mg of the active ingredient proanthocyanidin (PACs). Primary outcome was reducing the number of symptomatic CA-UTIs. This was defined by ≥103 (cfu)/mL of ≥1 bacterial species in a single catheter urine specimen and signs and symptoms compatible with CA-UTI. Secondary outcomes included bacterial counts and resistance patterns to antibiotics.Results: Thirty-four patients were enrolled in the trial; 22 patients (mean age 77.22 years, 77.27% were men) completed the study. Cranberry was effective in reducing the number of symptomatic CA-UTIs in all patients (n=22). Resistance to antibiotics was reduced by 28%. Furthermore, colony counts were reduced by 58.65%. No subjects had adverse events while taking cranberry.Conclusions: The cranberry supplement reduced the number of symptomatic CA-UTIs, antibiotic resistances, and major causative organisms in this cohort. Larger, placebo-controlled studies are needed to further define the role of cranberry in CA-UTIs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.256
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2017
Admission routes1
Has abstractyes

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