Abstract T P11: What Clinical Factors Predict An Intracranial Arterial Stenosis Responsible For An Intracranial Larger Artery Occlusion Treated By Endovascular Therapy?
Bibliographic record
Abstract
Objectives: In Asian countries where intracranial arterial stenosis (ICAS) is a common etiology regarding large artery intracranial occlusions in stroke. We sought to identify any clinical, laboratory and baseline imaging variables that may predict ICAS prior to endovascular treatment compared with angiographically-defined embolism (ADE). Methods: Patients were included if they had large cerebral artery occlusion in stroke on CT angiography and undertook transfemoral cerebral angiography, and if their onset to puncture time was within 8 hours. We defined ICAS and ADE by transfemoral cerebral angiography. ICAS was defined as fixed significant (> 50%) focal stenosis in the occlusion site, which could be seen in the final angiography or during the procedure of endovascular treatment. ADE was defined by no focal stenosis was evident after some recanalization achieved. Patients were excluded if their cause of stroke was associated or combined with other etiologies. Results: Finally, a total of 157 patients were included for this study. Table shows comparisons of clinical laboratory and imaging characteristics between ICAS and ADE groups. Patients in ICAS group were younger and male-predominant than in those in ADE group. Dyslipidemia and smokers were more frequent in ICAS group. Total cholesterol level was higher in ICAS group than in ADE group. Compared to ADE an ICAS was much more common in the posterior circulation 11/30 versus only 12/127 in the anterior circulation (p<0.001). Independent predictors of ICAS on multivariable analysis were male gender (odds ratio 6.34 [95% CI, 1.23-32.85], p=0.028), posterior circulation involvement (5.37 [1.62-17.82], p=0.006) and total cholesterol level (1.02 [1.004-1.033], p=0.012). Conclusion: The neurointerventionalist should prepare for the possibility of ICAS when performing endovascular treatment especially with posterior circulation occlusions in a Korean population.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".