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Using bispectral index monitoring to gauge depth of sedation/analgesia

2016· article· en· W3173090242 on OpenAlexaff
Theadoshia Mitchell-Hines, Kristi Ellison, Scott Willis

Bibliographic record

VenueNursing Critical Care · 2016
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsMitel (Canada)
Fundersnot available
KeywordsBispectral indexSedationMedicineElectrical cardioversionAnesthesiaIntensive care medicineCardioversionInternal medicineAtrial fibrillation

Abstract

fetched live from OpenAlex

In Brief Using a specialized bedside electroencephalographic monitor, clinicians can achieve individualized levels of moderate sedation/analgesia with predictive targeted brain wave values. Called the bispectral index (BIS) monitor, this noninvasive device can be used without assistance from highly specialized clinicians. This article explains how BIS monitoring works and describes the authors' experience with BIS monitoring in patients undergoing elective synchronized electrical cardioversion. Using a specialized bedside monitor, clinicians can achieve individualized levels of moderate sedation/analgesia with predictive targeted brain wave values. Called the bispectral index monitor, this noninvasive device can be used without assistance from highly specialized clinicians.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.327

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 head0.091
GPT teacher head0.418
Teacher spread0.327 · 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 designObservational
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

Citations4
Published2016
Admission routes1
Has abstractyes

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