A Survey on Monitoring and Management of Cerebral Vasospasm and Delayed Cerebral Ischemia After Subarachnoid Hemorrhage: The Mantra Study
Bibliographic record
Abstract
INTRODUCTION: Cerebral infarction from delayed cerebral ischemia (DCI) is a leading cause of poor neurological outcome after aneurysmal subarachnoid hemorrhage (aSAH). We performed an international clinical practice survey to identify monitoring and management strategies for cerebral vasospasm associated with DCI in aSAH patients requiring intensive care unit admission. METHODS: The survey questionnaire was available on the European Society of Intensive Care Medicine (May 2021-June 2022) and Neurocritical Care Society (April - June 2022) websites following endorsement by these societies. RESULTS: There were 292 respondents from 240 centers in 38 countries. In conscious aSAH patients or those able to tolerate an interruption of sedation, neurological examination was the most frequently used diagnostic modality to detect delayed neurological deficits related to DCI caused by cerebral vasospasm (278 respondents, 95.2%), while in unconscious patients transcranial Doppler/cerebral ultrasound was most frequently used modality (200, 68.5%). Computed tomography angiography was mostly used to confirm the presence of vasospasm as a cause of DCI. Nimodipine was administered for DCI prophylaxis by the majority of the respondents (257, 88%), mostly by an enteral route (206, 71.3%). If there was a significant reduction in arterial blood pressure after nimodipine administration, a vasopressor was added and nimodipine dosage unchanged (131, 45.6%) or reduced (122, 42.5%). Induced hypertension was used by 244 (85%) respondents as first-line management of DCI related to vasospasm; 168 (59.6%) respondents used an intra-arterial procedure as second-line therapy. CONCLUSIONS: This survey demonstrated variability in monitoring and management strategies for DCI related to vasospasm after aSAH. These findings may be helpful in promoting educational programs and future research.
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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".