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Record W2776270186 · doi:10.5152/tud.2017.81592

The role of the neurometer CPT/C in sacral neuromodulation

2017· article· en· W2776270186 on OpenAlexaff
Abdullah Ghazi, Malak Abuzgaya, Mai Banakhar, Magdy Hassouna

Bibliographic record

VenueUrology Research and Practice · 2017
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsMedicineSacral nerve stimulationImplantNeuromodulationPercutaneousSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Objective: The aim of the current research project was to study the role of the Neurometer® as a tool to predict responders to sacral neuromodulation therapy (SNM). Material and methods: This was a prospective, open study in male and female patients, aged 18 and over with voiding dysfunction [non-obstructive retention and/or frequency/ urgency syndrome]. The first group underwent a screening test to evaluate percutaneous nerve functions (PNE) and to determine whether they are candidates for SNM with the InterStim®. Prior to PNE testing, all patients were evaluated with the pain tolerance test (PTT) using the electro-diagnostic Neurometer® CPT/C device. An InterStim® implant was placed in patients who were responders to PNE testing underwent. On the other hand, non-responders underwent a staged implant placement. The second group consisted of patients who already had InterStim® implanted for voiding dysfunction. During the routine office follow-up, the patients implanted with Interstim® underwent a PTT using the Neurometer® CPT/C device. All the testing using the Neurometer CPT/C was performed on the day of the PNE for the first group, and the day of the routine follow-up visit for the second group. All of the results for the Neurometer® testing were kept blinded from the PNE results, and those of the outcome of the follow-up visit. The study received approval by the Research Ethics Board of the University Health Network (No. 14-8196). Results: We recruited a total of 123 patients. The results presented here include 110 patients who completed the study, 48 of whom were in the first group, and 62 in the second group. The statistical analysis used was as follows: Group 1: Simple linear regression analysis and the linear discriminate analysis were preformed. It was found that for patients without the InterStim® implant with a combined CPT/CPD of 800 and above, the Neurometer® could predict the test screening results with an accuracy of 71%. Group 2: Same analysis and tests were conducted for patients with the InterStim® implant, and the results showed that if the patient had a combined CPT/CPD of 600 and above, the Neurometer® could predict the patients satisfaction or dissatisfaction with an accuracy of 72%. Conclusion: Neurometer® may play a role in predicting test trial positive responders and patient satisfaction after the placement of InterStim® implant. Cite this article as: Ghazi AA, Abuzgaya M, Banakhar M, Hassouna M. The role of the neurometer CPT/C in sacral neuromodulation. Turk J Urol 2018; 44(1): 70-4.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.099
GPT teacher head0.444
Teacher spread0.345 · 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
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
Published2017
Admission routes1
Has abstractyes

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