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MP07-01 A BRIEF QUESTIONNAIRE TO ASSESS SYMPTOMS IN NEUROGENIC BLADDER: THE NBSS-SF

2019· article· en· W2941769589 on OpenAlexaboutno aff
Blayne Welk, John T. Stoffel, Sean P. Elliott, Sara Lenherr, Chong Zhang, Jeremy B. Myers

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCronbach's alphaNeurogenic bladder dysfunctionPhysical therapyUrinary incontinenceQuality of life (healthcare)GynecologyUrinary systemUrologyInternal medicinePsychometricsClinical psychology

Abstract

fetched live from OpenAlex

INTRODUCTION AND OBJECTIVES: Conventional PCNL has been commonly used for large kidney stones for a long time.New possibilities of minimally invasive operative techniques emerged with technological improvement of the instruments.Micro-PCNL has acquired popularity in recent years due to its high efficiency and low complications rate.Kidney stones less than 1.5 cm, low calyceal stones, and stones in calyceal diverticulum are the main indications for micro-PCNL.Considering the small size of the "all-seeing-needle", stone disintegration can be achieved mostly by means of laser energy.Holmium laser is typically used for this procedure.New Thulium SuperPulse Fiber Laser (TSPFL) with wavelength 1940 nm has been developed, cleared, and introduced into the clinical practice in 2016 (IRE-Polus, RF).The aim of the study is to evaluate stone free-rate and safety of Micro-PCNL with TSPFL.METHODS: Since June 2018 till October 2018, Micro-PCNL has been performed in 14 patients (8 females, 6 males, age range 30 to 71) with single kidney stones.All patients underwent preoperative ultrasound and CT-scan with stone density evaluation.Location of stones was as follows: renal pelvis -6 cases, low calyx -6 cases and middle calyx -2 cases.Stone size varied between 7 and 19 mm.Stone density varied from 560 to 1380 HU.All operations started with kidney catheterization and ureteral sheath 10-12F placement for passive fragment evacuation during the procedure.We performed Micro-PCNL with special PolyDiagnost micro-optics with resolution 10000 pixels passed through the working sheath of the puncture needle 4.85F.Stones were fragmented and dusted with TSPFL (FiberLase U2, IRE-Polyus, RF) with the use of the 200 mm laser fiber.The laser mode for fragmentation was 1-1.5 J and 15-30 Hz and for dusting 0.1-0.3J and 50-100 Hz.Control exam with kidney CT-scan was performed 30 days after surgery.RESULTS: The following criteria for stone-free status were adopted: 1) no residuals exceeding 3 mm and 2) no need for auxiliary procedures.Stone free-rate was in 92.8% (13 cases) 30 days after surgery.Operation time was variable and ranged between 55 and 105 min.Stone fragmentation time (or lasering time) was 8Æ6 min.Complication rate was low and included postoperative infection in one patient with significant bacteriuria and haematuria in one patient with coagulopathy.No serious bleeding or tissue perforation was observed during operation.The double-J stent was placed in one patient with renal colic because of "steinstrasse" developing after the surgery.This patient underwent ESWL afterwards.Postoperative stay was 3.4Æ1.2days.CONCLUSIONS: Micro-PCNL conducted with the new TSPFL is effective and safe for the endourological management of renal stone disease.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0350.013

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 head0.015
GPT teacher head0.277
Teacher spread0.262 · 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 designNot applicable
Domainnot available
GenreMethods

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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