Towards a Reduced-Order Model for Anesthesia: Identification of Propofol-Remifentanil Effect as a Single Synergic Drug
Bibliographic record
Abstract
For personalized anesthesia, it is crucial to have accurate pharmacokinetic and pharmacodynamic (PK/PD) models of anesthetic drugs. Typically, total intravenous anesthesia (TIVA) relies on the combined administration of propofol and remifentanil, which affect the bispectral index (BIS). This paper proposes a model reduction method that exploits the use of a fixed administration ratio between propofol and remifentanil, which is consistent with the clinical practice. Using a fourth-order ARX model, we estimate the effect-site concentration and fit the BIS response using a Hill function, thus implicitly considering the coadministration as if it was a single drug. The identification process is performed using a branch and bound approach, leveraging BIS data obtained during the induction of anesthesia. The identified model is then validated during the maintenance phase of anesthesia against models that are usually employed in clinical practice, where propofol and remifentanil are separately modeled. Results obtained on real BIS data show the effectiveness of the proposed approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".