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Record W2737963204 · doi:10.1213/xaa.0000000000000576

Closed-Loop Control of Total Intravenous Anesthesia During Significant Intraoperative Blood Loss

2017· article· en· W2737963204 on OpenAlexaff
Sonia M. Brodie, Matthias Görges, J. Mark Ansermino, Guy A. Dumont, Richard N. Merchant

Bibliographic record

VenueA & A Case Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAnesthesiaElectroencephalographyMedicineHypnosisPerfusionClosed loopDosingBurst suppressionCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Closed-loop control of anesthesia based on continuous feedback from processed electroencephalography adjusts drug dosing to target a desired depth of hypnosis during dynamic clinical circumstances, freeing the anesthesiologist to focus on more complex tasks. We describe a case of closed-loop control of total intravenous anesthesia in which a sudden loss of blood required immediate intervention. This case illustrates that closed-loop control of drug delivery maintained an appropriate depth of hypnosis during a rapidly changing surgical situation, and that processed electroencephalography may be a useful adjunct indicator for cerebral hypoperfusion.

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 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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.823

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 studies0.0000.000
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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations3
Published2017
Admission routes1
Has abstractyes

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