Risk, Types, and Severity of Intracranial Hemorrhage in Patients With Symptomatic Carotid Artery Stenosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We sought to report the occurrence and risk factors of intracranial hemorrhage during long-term follow-up of patients with internal carotid artery stenosis, with and without carotid endarterectomy. METHODS: From the prospective data of the North American Symptomatic Carotid Endarterectomy Trial, 3 types of intracranial hemorrhage were recognized: petechiae within infarction (PTI), intracerebral hematoma (ICH), and subarachnoid hemorrhage (SAH). The 30-day and 5-year risks of intracranial hemorrhage (PTI or ICH) were estimated from Kaplan-Meier event-free survival curves. Cox proportional-hazards regression modeling was used to identify risk factors. RESULTS: Of 1039 strokes that occurred in 749 of 2885 patients during an average follow-up of 5 years, there were 24 PTIs, 14 ICHs, and 1 SAH. The 5-year risk of intracranial hemorrhage was 1.7% in both medically and surgically treated patients, but the 30-day risk of 0.64% in surgically treated patients was 10 times higher than the risk of 0.07% in medically treated patients (P=0.01). Approximately 50% of all intracranial hemorrhages were either disabling or fatal, and ICHs were more likely to be fatal than PTIs. Old age, a history of hypertension, intermittent claudication and smoking, and infarct on brain images were risk factors for intracranial hemorrhage in medically treated patients, whereas diabetes mellitus was the sole risk factor in surgically treated patients. CONCLUSIONS: Intracranial hemorrhages are uncommon in patients with internal carotid artery stenosis but are associated with high mortality and morbidity. The risk factors for intracranial hemorrhage are different between medically and surgically treated 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 | 0.000 | 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 | 0.001 | 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".