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Record W2947125227 · doi:10.1097/aco.0000000000000747

Neuroanesthesia and outcomes

2019· review· en· W2947125227 on OpenAlexaff
Alana M. Flexman, Tianlong Wang, Lingzhong Meng

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2019
Typereview
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsUniversity of British ColumbiaVancouver General Hospital
Fundersnot available
KeywordsMedicineAnestheticHyperventilationIntensive care medicineCraniotomyHypocapniaAnesthesia

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The objective of this review is to identify outstanding topics most relevant to neuroanesthesia practice and patient outcomes. We discuss the role of awake craniotomy, choice of general anesthetic agents, monitoring of anesthetic 'depth', mannitol-induced diuresis, neurophysiological monitoring, hyperventilation, and cerebral hypoperfusion. RECENT FINDINGS: Awake craniotomy, although a technique likely underused, is associated with enhanced recovery after surgery and prolonged survival after brain tumor resection compared with surgery under general anesthesia. The choice of general anesthetic must balance patient and surgical factors. Although propofol may be associated with favorable oncologic outcomes, currently available retrospective evidence does not specifically address neurosurgical patients. Both the definition and monitoring of anesthetic 'depth' remains elusive. Neuroanesthesiologists need to recognize and manage intraoperative light anesthesia in a timely fashion. Further evidence related to the optimal management of mannitol-induced diuresis and hyperventilation in neurosurgical patients is needed. Contemporary neurophysiological monitoring can reasonably detect intraoperative neurologic injury; however, its effect on patient outcome is unclear. Finally, cerebral hypoperfusion without stroke may be common; however, the clinical significance requires further investigation. SUMMARY: We provide an overview of several topics that are relevant to neuroanesthesia practice and patient outcomes based on evidence, opinions, and speculations. Our review highlights the need for further outcome-oriented studies to specifically address these clinically relevant issues.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.303
GPT teacher head0.461
Teacher spread0.157 · 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 designNot applicable
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

Citations9
Published2019
Admission routes1
Has abstractyes

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