Dissecting aneurysm in poor-grade subarachnoid hemorrhage
Bibliographic record
Abstract
OBJECTIVE: Intracranial dissecting aneurysms (DAs) are rare and challenging lesions, often associated with high rates of rebleeding and poor clinical outcomes. There is limited evidence regarding optimal treatment strategies, timing, and outcomes, especially in the context of poor-grade subarachnoid hemorrhage (pSAH). The authors aimed to describe the clinical features and treatment outcomes of patients with DAs included in a national multicentric registry of pSAH and to identify independent outcome predictors within this subpopulation. METHODS: The authors conducted a retrospective analysis of prospectively collected data from the multicenter Poor-Grade Aneurysmal Subarachnoid Hemorrhage (POGASH) registry, including consecutive patients admitted between January 1, 2015, and June 30, 2024. Poor grade was defined as a pretreatment World Federation of Neurosurgical Societies grade IV-V. Outcomes were assessed using the modified Rankin Scale. DAs were classified according to the Mizutani classification. RESULTS: Of the 693 consecutive pSAH patients included in the registry, data from 60 patients with DA were analyzed. Among the 54 treated patients, 88.9% underwent endovascular treatment (vessel occlusion [48%], flow diversion [26%], and coiling [26%]), while 11.1% were treated surgically. The median (IQR) time to treatment was 6 (4-9) hours from symptom onset. Rebleeding occurred in 23.3% of patients, significantly more frequently than in the overall cohort (p < 0.048). Rebleeding independently predicted in-hospital mortality (adjusted OR 7.4; 95% CI 1.5-35.1; p = 0.011) and long-term disability (adjusted OR 0.08; 95% CI 0.007-0.98; p = 0.04). Internal carotid artery blister aneurysms were independently associated with rebleeding (adjusted OR 8; 95% CI 1.2-50; p = 0.027). CONCLUSIONS: In the context of pSAH, DAs are characterized by distinct clinicoradiological features and carry a significant risk of ultra-early rebleeding, which strongly influences clinical outcome. These findings suggest a potential benefit of ultra-early or immediate treatment in this patient population, pending further validation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".