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Record W2537213285 · doi:10.1109/embc.2016.7591392

Co-activation of saphenous nerve fibers: A potential therapeutic mechanism of percutaneous tibial nerve stimulation?

2016· article· en· W2537213285 on OpenAlexafffund
Christopher W. Elder, Paul B. Yoo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsPercutaneousOveractive bladderMedicineStimulationTibial nerveSaphenous nerveConcomitantTherapeutic effectSurgeryUrologyAnesthesiaInternal medicinePathology

Abstract

fetched live from OpenAlex

Percutaneous tibial nerve stimulation (PTNS) is a minimally invasive and effective treatment for overactive bladder (OAB). However, clinical trials show that positive therapeutic outcomes among patients are difficult to predict (failure rate = 35% to 50%). Inconsistencies in the stimulation amplitudes used clinically and those used in preclinical animal studies led us to hypothesize that OAB therapy involves a secondary bladder-inhibitory pathway. In this paper, we implemented and tested a computer model of the human lower leg that investigated the differential activation of the saphenous nerve (SAFN) and tibial nerve (TN) during percutaneous electrical stimulation. Our preliminary findings show that concomitant activation of SAFN branches occurs during PTNS, which suggests the possibility that the SAFN may influence the clinical outcome of treatment.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.030
GPT teacher head0.314
Teacher spread0.284 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

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