Closed-loop instrumental variable identification of propofol anesthesia
Bibliographic record
Abstract
One of the challenges in the development of high-performance closed-loop anesthetic drug delivery systems is the lack of accurate models. Physiological models have limited accuracy and drug effect varies largely between patients, while data-driven modeling of individual responses is challenging due to limited excitation and disturbances. This paper proposes a multi-input single-output (MISO) approach to deal with the effect of disturbances by identifying a plant model as well as a disturbance model. Furthermore, a MISO extension to closed-loop instrumental variable (IV) identification is proposed. Closed-loop IV methods are consistent without the need for identification of intermediate variables or noise-model parameters. Identification of fewer parameters is expected to be advantageous in this application where excitation is limited. The proposed approach is compared to closed-loop prediction-error methods. IV estimation achieved similar performance to a tailor-made parametrization. Bias in direct output-error (OE) estimates due to noise is limited. Closed-loop methods that require a controller description or that introduce additional computational complexity do not significantly improve model accuracy compared to direct OE estimation in this application.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".