Abstract 4358037: Barriers and Strategies in Neurovascular Care for Ruptured and Unruptured Intracranial Aneurysms During Awake Craniotomy: A Systematic Review
Bibliographic record
Abstract
Background: Awake craniotomy for ruptured and unruptured intracranial aneurysms poses distinct neurovascular challenges. Identifying key barriers and strategies is essential to improve outcomes, yet current evidence is limited, underscoring the need for systematic evaluation. Research Questions: What are the main barriers and strategies in the neurovascular care of ruptured and unruptured intracranial aneurysms during awake craniotomy? Methods: A systematic search was conducted on May 5, 2025, in PubMed, Scopus, Embase, and Web of Science, without language restrictions. This was supplemented by gray literature identified from reference lists and the first 10 pages of Google Scholar. The review followed PRISMA 2020 guidelines, and study quality was assessed using the Newcastle-Ottawa Scale (NOS). Two trained reviewers independently screened studies after pilot training. Data synthesis was descriptive. Results: The final analysis included 12 observational studies with 119 patients (58.4% unruptured, 25% ruptured, 8.3% mixed, and 8.3% unreported status), primarily in adults(91.7%) at university hospitals (75%). All cases reported "good" surgical outcomes, though 33.3% had transient or permanent sequelae. The most prevalent barriers were intraoperative patient discomfort (50%), anesthetic management challenges (41.7%), and lack of specialized training or advanced equipment (25%). Successful strategies included multidisciplinary team collaboration (58.3%), real-time functional monitoring via awake testing (100%), and intraoperative ultrasound (33.3%). Studies originated from the USA (41.7%), Europe (25%), and Asia (16.7%), revealing geographic disparities in resource availability. These findings demonstrate the procedure's feasibility while highlighting the need for standardized protocols to address technical and logistical challenges. Study quality assessed by tool NOS revealed: High quality (33.3%), Moderate quality (50%), and Low quality (16.7%). Conclusions: Awake craniotomy is a feasible and promising approach for managing intracranial aneurysms. However, its widespread adoption is hindered by significant implementation barriers. Further high-quality studies are needed to validate current strategies, evaluate outcomes, and ensure their safe integration into neurovascular care.
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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 | 0.018 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".