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Record W2122214470 · doi:10.1177/108925320200600106

Neuroprotection During Carotid Endarterectomy

2002· article· en· W2122214470 on OpenAlexaff
Jeff Granton, Adrian W. Gelb

Bibliographic record

VenueSeminars in Cardiothoracic and Vascular Anesthesia · 2002
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineCarotid endarterectomyCerebral perfusion pressurePerioperativeTranscranial DopplerEndarterectomyShunt (medical)Stroke (engine)Intensive care medicineNeuroprotectionAnesthesiaCarotid arteriesSurgeryCerebral blood flowCardiologyInternal medicine

Abstract

fetched live from OpenAlex

The goal of neuroprotection during carotid endarterectomy is a reduction in the frequency and severity of perioperative stroke. This includes cerebral ischemic events secondary to hypoperfusion during cross clamping, emboli, or both. However, rational use of protective techniques requires that patients at risk first be identified. This process begins with a thorough preoperative assessment, including neurological status and angiographic findings. lntraoperative monitoring is the next step in the identification. This can include the awake patient, electroencephalogram, transcranial Doppler, stump pressure or combinations of these. Unfortunately, evidence is lacking to demonstrate that any of these modalities is superior to another or to no monitoring at all. Finally, when a patient is at risk, a protective technique needs to be chosen. The use of surgical shunt placement has received mixed acceptance from surgeons performing these procedures. Barbiturate coma and anticoagulation may offer benefit but come with inherent risks as well. Maintenance of a high mean arterial pressure with vasopressors and fluids may also help improve collateral flow.

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 score0.047
Threshold uncertainty score0.953

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 head0.009
GPT teacher head0.227
Teacher spread0.218 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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