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Anesthesia for neuroradiology

2005· article· en· W2007907929 on OpenAlexaff
Jee Jian See, Pirjo Manninen

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2005
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsUniversity of TorontoUniversity Health NetworkToronto Western Hospital
Fundersnot available
KeywordsMedicineInterventional neuroradiologyNeuroradiologyNeurosurgeryInterventional radiologyNeurologyMagnetic resonance imagingCarotid arteriesMedical physicsIntensive care medicineRadiologySurgery

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The role of anesthesia outside the operating room is rapidly expanding and evolving alongside with the advances in interventional neuroradiology. Increasingly complex diagnostic and therapeutic neuroradiological procedures are being performed on sicker patients. This review provides an overview of the principles of anesthetic management and summarizes recent advances in interventional neuroradiology. RECENT FINDINGS: There are many new areas of development in interventional neuroradiology, but each also brings with it controversy. Use of newer agents for anesthesia and for anticoagulation may change the intraoperative management of patients. The role of neurophysiological monitoring during endovascular procedures is still to be validated. The optimal mode of treating cerebral aneurysms is still being debated. There has been increasing interest in and evidence of the efficacy of carotid artery stenting in the treatment of carotid artery disease. The utility of intraoperative magnetic resonance imaging in neurosurgery is expanding rapidly. SUMMARY: Providing anesthesia in the interventional neuroradiology suite continues to be a challenge to the anesthesiologist. Understanding the anesthetic constraints and complexities and keeping abreast of the current developments in neuroradiology are crucial in ensuring the maximal benefits to and safety of patients.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.513

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 head0.048
GPT teacher head0.347
Teacher spread0.299 · 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

Citations22
Published2005
Admission routes1
Has abstractyes

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