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Record W2125715509 · doi:10.1109/iembs.2009.5332682

Implantable functional gastrointestinal neurostimulation

2009· article· en· W2125715509 on OpenAlexaff
Alexander S. Jurkov, Alvaro Arriagada, Martin P. Mintchev

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPeristalsisNeurostimulationGastroparesisGastric emptyingMotor dysfunctionStimulationBiomedical engineeringStomachMedicineInternal medicine

Abstract

fetched live from OpenAlex

Neural Gastrointestinal Electrical Stimulation (NGES) is a new microprocessor-based method for invoking gastric or colonic contractions by generating multi-channel, high energy, high frequency waveforms. It has been shown that when applied to the lower stomach, NGES offers the possibility for enhancing propulsive peristalsis for the treatment of gastric motor dysfunctions, or for producing retrograde peristalsis for the treatment of obesity. When applied to the colon, NGES can be utilized either for propulsive control in severe constipation or for invoked retrograde contractility. This paper briefly discusses the implementation of an implantable neurostimulator and summarizes the performance of the NGES technique in acute tests on experimental animals and humans, and in chronic tests on animals. These experimental tests indicate that NGES is successful in accelerating gastric emptying of both liquids and solids, and in producing strong, externally-controlled, retrograde contractions.

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 score0.002
Threshold uncertainty score0.008

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 head0.024
GPT teacher head0.257
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

Citations1
Published2009
Admission routes1
Has abstractyes

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