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Record W2579738600 · doi:10.29007/8drm

Benchmark Problem: A PK/PD Model and Safety Constraints for Anesthesia Delivery

2018· article· en· W2579738600 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEPiC series in computing · 2018
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBenchmark (surveying)PropofolHypnosisComputer scienceUnconsciousnessPharmacodynamicsPharmacokineticsNonlinear systemCode (set theory)AnesthesiaMedicinePharmacologyPhysicsProgramming language

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

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

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 head0.025
GPT teacher head0.283
Teacher spread0.258 · 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