MétaCan
Menu
Back to cohort

Electrical Bio-Stimulation, Practical Safety for Circuits in Clinical Use

2025· article· W4417169772 on OpenAlexaff
Éric Bharucha, Simon Tremblay, Stéphane Hamel, Benoit Gosselin, Younès Messaddeq

Bibliographic record

Venuenot available
Typearticle
Language
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsABB (Canada)Université Laval
Fundersnot available
KeywordsSalientElectronic circuitKey (lock)Intrinsic safetyCompliance (psychology)Normative

Abstract

fetched live from OpenAlex

This paper presents the salient features for modern safety compliance requirements in implantable stimulators, a topic largely absent from academic and experimental publications. It also presents practical stimulation circuit design aspects. In addition, we discuss efficient ways of deploying stimulation to tissues while respecting normative requirements. The techniques are used to demonstrate the integration of multiple channels into a compact hybrid module that generates biphasic pulses useable for regulatory approved implants.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.004

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 head0.133
GPT teacher head0.423
Teacher spread0.289 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicNeuroscience and Neural EngineeringFrench-language works237,207