Abstract TP222: Frequency of Intracranial Arterial Imaging and its Yield in Consecutive Patients with Non-lacunar Cryptogenic Ischemic Stroke: ESUS Global Registry
Bibliographic record
Abstract
Background: Atherosclerotic stenosis of large IC arteries is an important cause of stroke. The prevalence of ICS in stroke population differs by ethnicity.We report the frequency of ICS among a global sample of patients with non-lacunar cryptogenic ischemic stroke (NLCIS) who did not have another identifiable cause for stroke i.e. cardioembolic, extracranial LAD, or other specific causes. Hypothesis: We hypothesized that the prevalence rates of ICS will differ according to global regions. Methods: Consecutive patients with recent ischemic stroke were retrospectively surveyed at 19 stroke centers in 19 countries to identify the frequency of IC imaging and its yield. Countries were grouped by World Bank regions. ICS was considered to be significant if there was >50% stenosis in the arteries proximal to the index stroke evidenced by MRA, CTA or TCD. Results: We identified a total of 2145 consecutive ischemic stroke patients among which 475 had NLCIS. IC arterial imaging was carried-out,on average, in 87% of patients. Of these 414 patients, 15% had stenosis proximal to the area of brain ischemia. The frequency of ICS among NLCIS patients was highest in East Asia (27%) and lowest in Pacific (4%). Patients with ICS in Latin America were significantly younger when compared to other 4 regions. Conclusion: IC arterial imaging is carried out in majority of stroke centers in patients with NLCIS, among whom the fraction of IS associated with ICS is substantial throughout the world, averaging about 15%. MRA / CTA had a higher yield than TCD. On average these patients have traditional vascular risk factors except for Latin American patients who are significantly younger with no vascular risk factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".