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Record W4412013696 · doi:10.1097/ana.0000000000001046

Temporary Intraoperative Cerebral Blood Flow Reduction to Facilitate Neurovascular Procedures

2025· article· en· W4412013696 on OpenAlexaff
Adele S. Budiansky, Tomasz Z. Polis, Kan Ma

Bibliographic record

VenueJournal of Neurosurgical Anesthesiology · 2025
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsUniversity of TorontoSt. Michael's HospitalOttawa Hospital
Fundersnot available
KeywordsMedicineNeurovascular bundlePerioperativeCerebral blood flowReduction (mathematics)Blood flowAnesthesiaSurgeryCardiology

Abstract

fetched live from OpenAlex

Temporary blood flow reduction is essential in the management of complex neurovascular lesions in both open and endovascular settings. This focused review examines the four principal techniques commonly used to achieve flow reduction for neurovascular procedures. Deep hypothermic circulatory arrest (DHCA) has largely become obsolete in recent years due to significant perioperative morbidity and the emergence of less invasive flow reduction strategies. Intravenous adenosine remains a popular option since it is readily available in the perioperative setting, though the hemodynamic response may be unpredictable because of interindividual dose-response variability. Rapid ventricular pacing (RVP) provides controlled, predictable flow reduction but requires advanced procedural planning. Endovascular balloon-assisted occlusion provides localized control in anatomically challenging areas under a hybrid neurosurgical-endovascular approach. To date, no single technique has demonstrated superiority over another, and the optimal strategy should be individualized based on lesion characteristics, institutional expertise, and available resources. Future research should focus on potential neuroprotective strategies during flow reduction and further characterize the safety and efficacy profiles of various flow reduction techniques through prospective cohort studies.

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 score0.503
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.262
Teacher spread0.237 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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