MétaCan
Menu
Back to cohort
Record W2926515870 · doi:10.1155/2019/2757862

Adult Neurogenic Lower Urinary Tract Dysfunction and Intermittent Catheterisation in a Community Setting: Risk Factors Model for Urinary Tract Infections

2019· review· en· W2926515870 on OpenAlexaff
Michael Kennelly, Nikesh Thiruchelvam, Márcio Augusto Averbeck, Charalampos Konstatinidis, Emmanuel Chartier‐Kastler, Pernille Trøjgaard, Rikke Vaabengaard, Andrei V. Krassioukov, Birte Petersen Jakobsen

Bibliographic record

VenueAdvances in Urology · 2019
Typereview
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsGF Strong Rehabilitation CentreUniversity of British Columbia
FundersColoplast
KeywordsUrinary systemMedicineIntensive care medicinePhysiologyInternal medicine

Abstract

fetched live from OpenAlex

A risk factor model for urinary tract infections in patients with adult neurogenic lower urinary tract dysfunction performing clean intermittent catheterisation was developed; it consists of four domains, namely, (1) general (systemic) conditions in the patient, (2) individual urinary tract conditions in the patient, (3) routine aspects related to the patient, and (4) factors related to intermittent catheters per se . The conceptual model primarily concerns patients with spinal cord injury, spina bifida, multiple sclerosis, or cauda equina where intermittent catheterisation is a normal part of the bladder management. On basis of several literature searches and author consensus in case of lacking evidence, the model intends to provide an overview of the risk factors involved in urinary tract infections, with specific emphasis to describe those that in daily practice can be handled and modified by the clinician and so come to the benefit of the individual catheter user in terms of fewer urinary tract infections.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.347
Teacher spread0.305 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations90
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in UrologySame topicUrinary Tract Infections ManagementFrench-language works237,207