Modelling blood pressure uncertainty for safety verification of propofol anesthesia
Bibliographic record
Abstract
Verifying safety of closed-loop drug delivery systems is a crucial step to obtain regulatory approval for such devices. Recently, we proposed a safety-preserving platform to formally verify the safety of closed-loop propofol anesthesia in the presence of uncertainty in patient models. This platform verifies that closed-loop anesthesia maintains the propofol concentration within the therapeutic window. To improve safety for at-risk patients, additional safety constraints on physiological variables, such as blood pressure, can be taken into account. To do so, an accurate description of the propofol effect on blood pressure as well as a comprehensive characterization of inter-patient variability are required. In this paper, we identify and validate a set of models characterizing the blood pressure response to propofol for 10 individual patients from clinical data. This model set shows uncertainty in the effect of propofol on blood pressure among at-risk patients. We use this model set to verify the safety of closed-loop propofol anesthesia subject to constraints on blood pressure using our safety verification platform. We show that there exists an admissible dose of propofol that maintains the propofol plasma concentration as well as blood pressure within safe bounds, without the need for additional treatment for hypotension. However, the dose of propofol which maintains safety, may not provide sufficient anesthesia for all patients and in critical cases treatment for hypotension would be required.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".