Benchmark Problem: A PK/PD Model and Safety Constraints for Anesthesia Delivery
Bibliographic record
Abstract
Hypnosis, or depth of unconsciousness, is one of the goals of general anesthesia. In this brief paper we provide a differential equation model of how propofol, a commonly used intravenous anesthetic drug, leads to hypnosis. The model has two components: the pharmacokinetics (PK) describing how the drug is metabolized by the body, and the pharmacodynamics (PD) describing how the drug effects the depth of hypnosis. This standard PK/PD model is a compartmental model, and is linear except for an internal time delay and a nonlinear output mapping. We discuss how this model can be simplified and/or complexified so as to take best advantage of the capabilities of a particular analysis method. One goal of developing such a model is to ensure patient safety during surgery, so we describe an example safety verification problem. Finally, in order to demonstrate that this benchmark is operational, we provide code to simulate one version of the system.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".