An Explicit Reference Governor Scheme for Closed-Loop Anesthesia
Bibliographic record
Abstract
This paper proposes a constrained control scheme for the control of the depth of hypnosis in clinical anesthesia. The proposed scheme guarantees overdosing prevention while taking into account infusion rate limits and safety constraints on the plasma concentration. The core idea is to formulate anesthesia as a constrained control problem and design a closed-form control scheme based on the explicit reference governor philosophy. More precisely, the proposed architecture consists of a stabilizing control loop and of an add-on control unit that is able to ensure the constraints satisfaction at all times. In this paper, this architecture has been implemented within the iControl system, a platform for clinical evaluation of control schemes. The proposed scheme is evaluated on a simulated surgical procedure for 44 patients. The results demonstrate that the proposed scheme can deliver propofol to yield induction time of (mean) 6.24 [min], while satisfying the imposed safety constraints.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".