P107/247 Diagnosis and endovascular management of vasospasm after aneurysmal subarachnoid hemorrhage – survey on real life practices
Bibliographic record
Abstract
<h3>Introduction</h3> Vasospasm and delayed cerebral ischemia (DCI) are the leading causes of morbidity and mortality after intracranial aneurysmal subarachnoid hemorrhage (aSAH). Vasospasm detection, prevention and management, especially endovascular management varies from center to center and lacks standardization. <h3>Aim of Study</h3> We aimed to evaluate this variability through an international survey on diagnosis and endovascular management of vasospasm. <h3>Methods</h3> A 100 question anonymous online survey was designed to evaluate the practice patterns between December 2021 and September 2022. Endovascular neurosurgeons, neuroradiologists and neurologists were contacted through email and professional societies (SNIS and ESMINT). Answers were recorded. <h3>Results</h3> A total of 201 physicians (25% [50/201] USA and 75% non-USA) completed the survey over 10 months, 42% had >7years of experience, 92% were male, median age was 40 (IQR 35–46). Both high-volume and low-volume centers were represented. Daily transcranial Doppler was the most common screening method (75%) for vasospasm. In cases of symptomatic vasospasm despite optimal medical management, endovascular treatment was directly considered by 58% physicians. The most common reason to initiate endovascular treatment was clinical deficits associated with proven vasospasm/DCI in 89%. The choice of endovascular treatment and its efficacy was highly variable. Nimodipine was the most common first-line intra-arterial therapy (40%). Mechanical angioplasty was considered the most effective endovascular treatment by 65% of neurointerventionalists. <h3>Conclusion</h3> Our study highlights the considerable heterogeneity among the neurointerventional community regarding vasospasm diagnosis and endovascular management. Randomized trials and guidelines are needed to improve standard of care, determine optimal management approaches and track. <h3>Disclosure of Interest</h3> Dr Guenego reports consultancy for Rapid Medical and Phenox
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".