Robust MISO Control of Propofol-Remifentanil Anesthesia Guided by the NeuroSENSE Monitor
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Bibliographic record
Abstract
This paper describes the design and evaluation of a controller for multi-input single-output (MISO) propofol-remifentanil anesthesia, guided by feedback from a measure of depth-of-hypnosis (DOH). DOH monitors are commonly used in clinical practice to guide anesthetic dosing, however, there is currently no widely accepted nociception monitor to guide remifentanil (analgesic) infusion. Variability in the DOH measure has been associated with insufficient analgesia, and feasibility of closed-loop control of both propofol and remifentanil infusion using DOH feedback has been demonstrated. However, DOH variability does not provide a measure of analgesia in the absence of stimulation. Consequently, control of the opioid-hypnotic balance is lost in control systems relying on DOH feedback alone. The proposed design overcomes this limitation by introducing a second, indirect control objective. This paper defines clinical design specifications to achieve adequate anesthesia in a wide range of clinical cases, proposes a modification of the habituating control framework, and presents methods to translate the clinical objectives into control objectives within this framework. The developed design methodology provides a controller that: 1) meets the clinical objectives; 2) is robust to interpatient variability, both in single-input single-output and MISO operation; 3) is robust to nonlinear drug interactions; 4) gives the user control of the opioid-hypnotic balance in the absence of stimulation and in the presence of input saturation; and 5) improves disturbance rejection following nociceptive stimulation. The MISO system performed as designed in 80 clinical cases.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it