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28.8 Multi-Modal Peripheral Nerve Active Probe and Microstimulator with On-Chip Dual-Coil Power/Data Transmission and 64 2<sup>nd</sup>-Order Opamp-Less ΔΣ ADCs

2021· article· en· W3135551580 on OpenAlexaff
Maged ElAnsary, Jianxiong Xu, José Sales Filho, Gairik Dutta, Liam Long, Aly Shoukry, Camilo Tejeiro, Chenxi Tang, Enver G. Kilinc, Jaimin Joshi, Parisa Sabetian, Samantha Unger, José Zariffa, Paul B. Yoo, Roman Genov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPeripheral nervous systemPeripheralStimulationNeuroscienceElectromagnetic coilComputer scienceMedicineCentral nervous systemEngineeringElectrical engineeringPsychology

Abstract

fetched live from OpenAlex

The peripheral nervous system (PNS) enables communication between the central nervous system and various organs, for example by conveying sensory information and relaying motor commands. Electrical stimulation of peripheral nerves has been shown effective in treating major intractable disorders ranging from autoimmune disorder to chronic pain. It acts on specific nerves and avoids significant side effects of most drugs. Closed-loop PNS neurostimulators offer the additional benefits of personalization and optimality of the treatment. Such medical devices infer physiological function from measurable nerve action potentials and deliver custom-tailored electrical stimulation to elicit desired clinical outcomes.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0310.008

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 head0.040
GPT teacher head0.273
Teacher spread0.233 · 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

Citations22
Published2021
Admission routes1
Has abstractyes

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