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Record W2526709932 · doi:10.5213/inj.1632536.268

Sacral Neuromodulation in Patients With a Cardiac Pacemaker

2016· article· en· W2526709932 on OpenAlexaff
Abdullah Ahmed Gahzi, Dean Elterman, Magdy Hassouna

Bibliographic record

VenueInternational Neurourology Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsMedicineNeuromodulationUrinary urgencyUrinary retentionCardiac pacemakerUrinary incontinencePercutaneousUrinary systemSacral nerve stimulationSurgeryBradycardiaAnesthesiaCardiologyOveractive bladderInternal medicineHeart rate

Abstract

fetched live from OpenAlex

The objective of this study was to describe our experience using sacral neuromodulation to treat urinary urgency, frequency, urge incontinence, and chronic urinary retention in patients with cardiac pacemakers. With the increasingly widespread use of InterStim for bladder function restoration, we are seeing more complex patients with multiple comorbidities, including cardiac conditions. Herein, we report 3 cases of individuals with cardiac pacemakers who underwent InterStim implantation to treat urinary conditions. This study is a case series of 3 patients with cardiac pacemakers who underwent sacral neuromodulation to treat refractory voiding dysfunction. The initial patient screening for InterStim therapy involved percutaneous nerve evaluation (PNE), in which a temporary untined lead wire was placed through the S3 foramen. Patients who did not respond to PNE proceeded to a staged implant. All patients in this study had a greater than 50% improvement of their urinary symptoms during the initial trial and underwent placement of the InterStim implantable pulse generator (IPG). Postoperative programming was done under electrocardiogram monitoring by a cardiologist. No interference was observed between the Inter- Stim IPG and the cardiac pacemaker. In this group of patients, sacral neuromodulation in the presence of a cardiac pacemaker appears to have been safe.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.016
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.255
Teacher spread0.247 · 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.

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

Citations8
Published2016
Admission routes1
Has abstractyes

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