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MP07-06 TELEMEDICINE AND HOME MONITORING OF BLADDER FUNCTION FOR MANAGEMENT OF URINARY TRACT INFECTION IN NEUROGENIC BLADDER & SPINAL CORD INJURY

2019· article· en· W2941330330 on OpenAlexaboutno aff
Lynn Stothers, Emily G. Deegan, Alex Kavanagh, Blayne Welk, Mark Nigro

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSpinal cord injuryUrinary systemSpinal cordUrinary bladderTelemedicineUrologyInternal medicine

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyUrodynamics/Lower Urinary Tract Dysfunction/Female Pelvic Medicine: Neurogenic Voiding Dysfunction (MP07)1 Apr 2019MP07-06 TELEMEDICINE AND HOME MONITORING OF BLADDER FUNCTION FOR MANAGEMENT OF URINARY TRACT INFECTION IN NEUROGENIC BLADDER & SPINAL CORD INJURY Lynn Stothers*, Emily Deegan, Alex Kavanagh, Blayne Welk, and Mark Nigro Lynn Stothers*Lynn Stothers* More articles by this author , Emily DeeganEmily Deegan More articles by this author , Alex KavanaghAlex Kavanagh More articles by this author , Blayne WelkBlayne Welk More articles by this author , and Mark NigroMark Nigro More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555090.56812.9cAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Urinary tract infection (UTI) is a serious complication of neurogenic bladder (NB), is associated with resistant organisms and causes autonomic dysreflexia & reduced quality of life. UTI diagnosis in NB is difficult due to sensory changes prohibiting reports of traditional LUTS. Near Infrared spectroscopy (NIRS) has previously reported diagnostic capabilities in pediatric UTI. Objective: examine telemedicine with home monitoring of bladder parameters including NIRS for early UTI diagnosis & symptom reduction. METHODS: Prospective cohort study (NLUTD with recurrent UTI). Subjects served as their own controls x 6 months run in then completed adaptive telemedicine. Measures at 3 time points in clinic included: Neurogenic Bladder Symptom Score (NBSS), SF–36 and Qualiveen–30, urine culture & bladder NIRS. Home-monitoring telemedicine conducted monthly x 6 months including BP, temperature, & urinalysis. A subset of 7 conducted home bladder NIRS for tissue saturation index (TSI%) during natural filling & x 30 min. post emptying. Asymptomatic urine cultures obtained at 3 time points & with symptomatic UTI had bacteria frozen for genetic analysis. RESULTS: N=62; 75% male, 25% female (median 49 & 46 yrs respectively) SCI 87% (ASIA 64%, B 20%, C 9%, D 3.7%, E 2%), MS 7%, spina bifida 5%, other injury 13%; median time since injury 16 yrs. Bladder management: CIC 64%, indwelling cath 29%, spontaneous voiding 7%. 2 used adaptive mouth technology to operate telemedicine independently. 190 urine cultures revealed 135 isolates; 18 strains, antimicrobial resistance (AMR) in 54%. 324 visits demonstrated 91% compliance. 7 SCI subjects completed home NIRS independently capturing TSI%. Urinary incontinence (UI) showed statistically significant reduction on NBSS following telemedicine (p=0.02). CONCLUSIONS: Subjects on telemedicine with home monitoring experienced a significant reduction in UI & high compliance with visits even in high tetraplegia. Home monitoring of blood pressure and acquisition of bladder NIRS was feasible. Telemedicine can improve NB management and increase healthcare access. Bladder NIRS in the SCI population is feasible and could provide an adjunctive method for early UTI detection in this population with high AMR. Source of Funding: Rick Hansen Foundation Vancouver, Canada; London, Canada; Vancouver, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e92-e92 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Lynn Stothers* More articles by this author Emily Deegan More articles by this author Alex Kavanagh More articles by this author Blayne Welk More articles by this author Mark Nigro More articles by this author Expand All Advertisement PDF downloadLoading ...

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.441
Threshold uncertainty score0.798

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.4410.177

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 head0.025
GPT teacher head0.311
Teacher spread0.286 · 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.

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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