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Record W4220784260 · doi:10.12968/bjcn.2022.27.4.162

Recent insights into catheter-related urinary tract infections

2022· article· en· W4220784260 on OpenAlexaff
Mark Greener

Bibliographic record

VenueBritish Journal of Community Nursing · 2022
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsWorld Federation of Science Journalists
Fundersnot available
KeywordsUrinary systemMicrobiomeMedicineBiofilmCatheterUrinary catheterBacteriaIntensive care medicineAntibioticsUrinary catheterizationMicrobiologyInternal medicineBioinformaticsSurgeryBiology

Abstract

fetched live from OpenAlex

Urinary tract infections (UTIs) commonly develop in people with urinary catheters. Inserting a catheter can damage the urothelial barrier and trigger the formation of a biofilm on the catheter that allows bacteria direct access to the bladder. Biofilms also protect bacteria from the immune system and reduce antibiotic effectiveness. In addition, a growing literature suggests that the urinary tract harbours bacteria even in people with negative conventional cultures. The urinary microbiome is highly individual. Nevertheless, changes in the urinary microbiome may identify individuals at risk of UTIs and, for example, suggest that a catheter should be replaced more frequently and, in turn, avoid the need for antibiotics. This article outlines the importance of biofilms in the development of catheter-related UTIs and introduces the urinary microbiome.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.027
GPT teacher head0.301
Teacher spread0.274 · 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.

Study designOther design
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

Citations2
Published2022
Admission routes1
Has abstractyes

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