Closed-Loop MISO Identification of Propofol Effect on Blood Pressure and Depth of Hypnosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Feasibility of closed-loop propofol anesthesia has been demonstrated in clinical trials; however, no closed-loop system is currently routinely used in clinical practice. Data from closed-loop clinical trials provide valuable information for system evaluation prior to market approval, including outlier behavior. To exploit this valuable data, closed-loop identification in the presence of nonzero disturbances is required. Disturbances due to stimulation are not zero mean and will cause bias in models identified from clinical data. In this paper, we use multiple-input single-output (MISO) closed-loop identification to take the response to disturbances during induction of anesthesia into account. It is shown that direct MISO output-error identification introduces limited bias for this specific closed-loop identification problem and the use of more complex closed-loop identification methods does not improve model accuracy. Using this approach, we identify and validate a set of models that describes both the blood pressure and the depth-of-hypnosis response to propofol infusion for patients at risk of cardiovascular suppression. Quantification of both these responses for the same patients provides a valuable basis for the design and evaluation of constrained control systems.
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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 | 0.000 | 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) | 0.000 | 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