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Record W3006077822 · doi:10.3389/fnsys.2020.00002

The Effects of Anesthetics on the Cortex—Lessons From Event-Related Potentials

2020· article· en· W3006077822 on OpenAlexaff
Dana Baron Shahaf, Gregory M. T. Hare, Goded Shahaf

Bibliographic record

VenueFrontiers in Systems Neuroscience · 2020
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsNeuroscienceEvent-related potentialEvent (particle physics)PsychologyCognitive scienceCognitionPhysics

Abstract

fetched live from OpenAlex

Consciousness while under general anesthesia is a dreadful condition. Various electroencephalogram (EEG)-based technologies have been developed, on the basis of empirical evidence, in order to identify this condition. However, certain electrophysiological phenomena, which seem strongly related with depth of anesthesia in some drugs, appear less consistent with those of other anesthetic drugs. There is a gap between the complexity of the phenomenon of consciousness and its behavioral manifestations, on the one hand, and the empirical nature of the reported electrophysiological markers, which are associated with it, on the other hand. In fact, such a gap might prevent us from progressing toward unified electrophysiological markers of consciousness while under anesthesia, which are applicable to all anesthetic drugs. We believe that there is a need to bridge this conceptual gap. Therefore, in this work, we will try to present a theoretical framework for such bridging. First, we suggest focusing on neuropsychological processes, which seem to have a clear role in the behavioral manifestations of consciousness while under anesthesia but seem, nevertheless, better defined than consciousness itself-such as perception and attention. Then, we suggest analyzing the effects of anesthesia upon these neuropsychological processes, as they are manifested in the EEG signal. Specifically, we will focus on the effects of anesthesia on event-related potentials (ERPs), which seem more easily associable with neuropsychological modeling.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.297
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.252
Teacher spread0.231 · 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 teacher head, 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

Citations9
Published2020
Admission routes1
Has abstractyes

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