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Record W2137164841 · doi:10.1002/acs.1087

Robust control of depth of anesthesia

2008· article· en· W2137164841 on OpenAlexafffund
Guy A. Dumont, Arturo Minor Martínez, J. Mark Ansermino

Bibliographic record

VenueInternational Journal of Adaptive Control and Signal Processing · 2008
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsControl theory (sociology)Robust controlPropofolPID controllerController (irrigation)Set pointDivergence (linguistics)AnesthesiaComputer scienceMedicineControl engineeringControl (management)Control systemEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This paper presents a systematic procedure to design both robust PID controllers and robust controllers based on fractional calculus (based on Commande Robuste d'Ordre Non Entier, or CRONE methodology) to regulate the hypnotic state of anesthesia with the intravenous administration of propofol. The objective of the controllers is to provide an adequate drug administration regimen for propofol to avoid under or over dosing of the patients. The controllers are designed to compensate for the patients inherent drug–response variability (uncertainty), to achieve good output disturbance rejection, and to attain good set point response. The performance of the controllers is assessed by calculating typical time domain measures and using the median PE, median absolute PE, divergence, and wobble. Copyright © 2008 John Wiley & Sons, Ltd.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.040
GPT teacher head0.266
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations162
Published2008
Admission routes2
Has abstractyes

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