Angiographic Vasospasm Is Strongly Correlated With Cerebral Infarction After Subarachnoid Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The long-standing concept that delayed cerebral infarction after aneurysmal subarachnoid hemorrhage results exclusively from large artery vasospasm recently has been challenged. We used data from the CONSCIOUS-1 trial to determine the relationship between angiographic vasospasm and cerebral infarction after subarachnoid hemorrhage. METHODS: We performed a post hoc exploratory analysis of the CONSCIOUS-1 data. All patients underwent catheter angiography before treatment and 9±2 days after subarachnoid hemorrhage. CT was performed before and after aneurysm treatment, and 6 weeks after subarachnoid hemorrhage. Angiograms and CT scans were assessed by centralized blinded review. Angiographic vasospasm was classified as none/mild (0%-33% decrease in arterial diameter), moderate (34%-66%), or severe (≥67%). Infarctions were categorized as secondary to angiographic vasospasm, other, or unknown causes. Logistic regression was conducted to determine factors associated with infarction. RESULTS: Complete data were available for 381 of 413 patients (92%). Angiographic vasospasm was none/mild in 209 (55%) patients, moderate in 118 (31%), and severe in 54 (14%). Infarcts developed in 6 (3%) of 209 with no/mild, 12 (10%) of 118 patients with moderate, and 25 (46%) of 54 patients with severe vasospasm. Multivariate analysis found a strong association between angiographic vasospasm and cerebral infarction (OR, 9.3; 95% CI, 3.7-23.4). The significant association persisted after adjusting for admission neurological grade and aneurysm size. Method of aneurysm treatment was not associated with a significant difference in frequency of infarction. CONCLUSIONS: A strong association exists between angiographic vasospasm and cerebral infarction. Efforts directed at further reducing angiographic vasospasm are warranted.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".