The Effects of Anesthetics on the Cortex—Lessons From Event-Related Potentials
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".