Method of Aneurysm Treatment Does Not Affect Clot Clearance After Aneurysmal Subarachnoid Hemorrhage
Bibliographic record
Abstract
BACKGROUND: Patients undergoing neurosurgical clipping or endovascular coiling of a ruptured aneurysm may differ in their risk of vasospasm. OBJECTIVE: Because clot clearance affects vasospasm, we tested the hypothesis that clot clearance differs in patients depending on method of aneurysm treatment. METHODS: Exploratory analysis was performed on 413 patients from CONSCIOUS-1, a prospective randomized trial of clazosentan for the prevention of angiographic vasospasm in patients with aneurysmal subarachnoid hemorrhage (SAH). Clot clearance was measured by change in Hijdra score between baseline computed tomography and one performed 24 to 48 hours after aneurysm treatment. Angiographic vasospasm was assessed by the use of catheter angiography 7 to 11 days after SAH, and delayed ischemic neurological deficit (DIND) was determined clinically. Extended Glasgow Outcome Score (GOSE) was assessed 3 months after SAH, and poor outcome was defined as death, vegetative state, or severe disability. Multivariable ordinal and binary logistic regression were used. RESULTS: There was no significant difference in the rate of clot clearance between patients undergoing clipping or coiling (P = .56). Coiling was independently associated with decreased severity of angiographic vasospasm (odds ratio [OR] 0.53, 95% confidence interval [CI] 0.33-0.86), but not with DIND or GOSE. Greater clot clearance decreased the risk of severe angiographic vasospasm (OR 0.86, 95% CI 0.81-0.91), whereas higher baseline Hijdra score predicted increased angiographic vasospasm (OR 1.17, 95% CI 1.11-1.23) and poor GOSE (OR 1.09, 95% CI 1.04-1.14). CONCLUSION: Aneurysm coiling and increased clot clearance were independently associated with decreased severity of angiographic vasospasm in multivariate analysis, although no differences in clot clearance were seen between coiled and clipped patients.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".