Physiological Signal Processing for Individualized Anti-nociception Management During General Anesthesia: a Review
Bibliographic record
Abstract
OBJECTIVE: The aim of this paper is to review existing technologies for the nociception / anti-nociception balance evaluation during surgery under general anesthesia. METHODS: General anesthesia combines the use of analgesic, hypnotic and muscle-relaxant drugs in order to obtain a correct level of patient non-responsiveness during surgery. During the last decade, great efforts have been deployed in order to find adequate ways to measure how anesthetic drugs affect a patient's response to surgical nociception. Nowadays, though some monitoring devices allow obtaining information about hypnosis and muscle relaxation, no gold standard exists for the nociception / anti-nociception balance evaluation. Articles from the PubMed literature search engine were reviewed. As this paper focused on surgery under general anesthesia, articles about nociception monitoring on conscious patients, in post-anesthesia care unit or in intensive care unit were not considered. RESULTS: In this article, we present a review of existing technologies for the nociception / anti-nociception balance evaluation, which is based in all cases on the analysis of the autonomous nervous system activity. Presented systems, based on sensors and physiological signals processing algorithms, allow studying the patients' reaction regarding anesthesia and surgery. CONCLUSION: Some technological solutions for nociception / antinociception balance monitoring were described. Though presented devices could constitute efficient solutions for individualized anti-nociception management during general anesthesia, this review of current literature emphasizes the fact that the choice to use one or the other mainly relies on the clinical context and the general purpose of the monitoring.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".