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Record W2077840301 · doi:10.1097/aco.0b013e3283404641

Monitoring of brain function in anesthesia and intensive care

2010· review· en· W2077840301 on OpenAlexaff
Hilary P. Grocott, Sophie N. Davie, Christine Fedorow

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2010
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineModalitiesPerioperativeIntensive careIntensive care medicineAnestheticMEDLINEAnesthesia

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Despite its obvious importance, the brain is inconsistently the focus of monitoring in anesthesia and intensive care settings. However, there are multiple modalities available to address cerebral monitoring that when acted upon, may improve perioperative outcomes. This review addresses the various brain monitoring options that can be integrated in anesthetic and intensive care practice in order to optimize perioperative outcomes. RECENT FINDINGS: Although numerous monitoring modalities are available, the level of evidence supporting each application is somewhat limited with few of the available monitors having been subjected to large-scale randomized trials. Despite this, they each may have a potential role to play in providing information that can be integrated to optimize care. SUMMARY: Using a comprehensive cerebral monitoring strategy may optimize outcomes in anesthetic and intensive care.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.077
GPT teacher head0.393
Teacher spread0.316 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations24
Published2010
Admission routes1
Has abstractyes

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