Temporary Intraoperative Cerebral Blood Flow Reduction to Facilitate Neurovascular Procedures
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".