Prevalence of Intracranial Atherosclerotic Disease in Patients with Low-Risk Transient or Persistent Neurologic Events
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: There are limited data on the prevalence and outcome of intracranial atherosclerotic disease in patients with low-risk transient or persistent minor neurologic events. We sought to determine the prevalence and risk factors associated with intracranial atherosclerotic disease in patients with low-risk transient or persistent neurologic events. MATERIALS AND METHODS: Participants with available intracranial vascular imaging from the Diagnosis of Uncertain-Origin Benign Transient Neurologic Symptoms (DOUBT) study, a large prospective multicenter cohort study, were included in this post hoc analysis. The prevalence of intracranial atherosclerotic disease of ≥50% was determined, and the association with baseline characteristics and DWI lesions was evaluated using logistic regression. RESULTS: We included 661 patients with a median age of 62 years (interquartile range, 53-70 years), of whom 53% were women. Intracranial atherosclerotic disease was found in 81 (12.3%) patients; asymptomatic intracranial atherosclerotic disease alone, in 65 (9.8%); and symptomatic intracranial atherosclerotic disease, in 16 (2.4%). The most frequent location was in the posterior cerebral artery (29%). Age was the only factor associated with any intracranial atherosclerotic disease (adjusted OR, 1.9 for 10 years increase; 95% CI, 1.6-2.5). Multivariable logistic regression showed a strong association between intracranial atherosclerotic disease and the presence of acute infarct on MR imaging (adjusted OR, 3.47; 95% CI, 1.91-6.25). CONCLUSIONS: Intracranial atherosclerotic disease is not rare in patients with transient or persistent minor neurologic events and is independently associated with the presence of MR imaging-proved ischemia in this context. Evaluation of the intracranial arteries could be valuable in establishing the etiology of such low-risk events.
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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) | 0.002 | 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".