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Record W2536878075 · doi:10.1109/embc.2016.7591717

Posterior tibial nerve stimulation using a wirelessly powered system in anesthetized cats

2016· article· en· W2536878075 on OpenAlexaff
Zainab Moazzam, Austin R. Duke, Paul B. Yoo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsStimulationOveractive bladderMedicineTibial nerveCATSImplantHindlimbAnesthesiaBiomedical engineeringSurgeryAnatomyInternal medicine

Abstract

fetched live from OpenAlex

Posterior Tibial Nerve Stimulation (PTNS) is an effective overactive bladder (OAB) therapy where electrical pulses are typically delivered once per week in a 12-week stimulation regime. While the mechanism of action remains unknown, effective long-term delivery of PTNS has recently become a subject of concern. To this end, a multi-contact electrode was surgically placed in the hind limb region of anesthetized cats to (1) investigate the feasibility of using a wirelessly powered system to stimulate PTN afferents and (2) characterize implant-driven effects of stimulation frequency on modulating bladder activity. Using an isovolumetric model, short-duration, supra-threshold stimulation trials were applied with frequencies ranging from 2-20 Hz. The results provide first pre-clinical evidence of frequency-dependent modulation of bladder function supporting the use of a novel therapeutic approach that can be clinically translated to potentially address multiple symptoms of lower urinary tract system.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.041
GPT teacher head0.329
Teacher spread0.287 · 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 designBench or experimental
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

Citations0
Published2016
Admission routes1
Has abstractyes

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