Event‐based control tuning of propofol and remifentanil coadministration for general anaesthesia
Bibliographic record
Abstract
In this study, the authors present robust tuning rules for an event‐based control architecture for the automatic regulation of the depth of hypnosis in anaesthesia. The authors' control system uses propofol and remifentanil coadministration as control variables and the bispectral index as controlled variable. The control system is based on a PIDPlus controller combined with an event generator that detects significant variations of the BIS signal, thus providing strong filtering of the noise. A fixed ratio between the drug infusions allows the anaesthesiologist to explicitly regulate the opioid–hypnotic balance of the anaesthesia. The tuning rules are developed by solving a min–max optimisation problem that optimises the worst‐case scenario over a given data set of patient models. A gain scheduling strategy yields optimal performance in both the induction and the maintenance phases of anaesthesia. Finally, through the Monte Carlo method, they validate the effectiveness of the proposed approach on a general population, and the robustness to the intra‐ and inter‐patient variability for different infusion balances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".