Coordinated semi‐adaptive closed‐loop control for infusion of two interacting medications
Bibliographic record
Abstract
Summary This paper presents a coordinated and semi‐adaptive closed‐loop control approach to the infusion of 2 interacting medications. The proposed approach consists of an upper‐level coordination controller and a lower‐level semi‐adaptive controller. The coordination controller recursively adjusts the reference targets based on the estimated dose‐response relationship of a patient to ensure that they can be achieved by the patient. The semi‐adaptive controller drives the patient outputs to the reference targets while estimating the patient's dose‐response relationship online. In this way, the controller is resilient to unachievable caregiver‐specified reference targets and responsive to the medication needs of individual patients. To establish the proposed approach, we developed the following: (1) a linear two‐input–two‐output dose‐response model; (2) a two‐input–two‐output semi‐adaptive controller to regulate the patient outputs while adapting high‐sensitivity parameters in the patient model; and (3) a coordination controller to adjust the reference targets that reconcile caregiver inputs and medication use. The proposed approach was applied to an example scenario in which cardiac output and respiratory rate are regulated via infusion of propofol and remifentanil in an in silico simulation setting. The results show that the coordinated semi‐adaptive control could (1) track achievable reference targets with consistent transient and steady‐state performance and (2) resiliently adjust the unachievable reference targets to achievable ones.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".