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Record W2765755209 · doi:10.1016/j.ifacol.2017.08.368

A Formally-Verified Safety System for Closed-Loop Anesthesia

2017· article· en· W2765755209 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.

Bibliographic record

VenueIFAC-PapersOnLine · 2017
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsClosed loopLoop (graph theory)Computer scienceAnesthesiaMedicineControl engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

The benefits of closed-loop control of anesthesia in terms of drug usage, robustness to inter-patient variability and postoperative outcomes have been demonstrated in a number of clinical studies. However, to obtain regulatory approval for such systems to be employed as medical devices in operating rooms, patient safety must be demonstrated. This paper formalizes a previously published safety system for closed-loop anesthesia using formal model verification techniques. This safety system specifies safety constraints on the patient states based on the therapeutic window of propofol. To verify feasibility of the safety constraints in all situations, a finite number of simulation scenarios can be performed. However, the formal methods verify the feasibility problem for all possible admissible inputs and states without the need for simulation. The formalized safety system for closed-loop anesthesia guarantees that the patient states stay within safety constraints.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.034
GPT teacher head0.306
Teacher spread0.272 · 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