E-065 Experience with neuroform atlas stenting as rescue endovascular treatment after failed mechanical thrombectomy secondary to intracranial atherosclerosis
Bibliographic record
Abstract
Background Patients with emergent large vessel occlusion secondary to intracranial atherosclerotic stenosis (ICAS-ELVO) who fail mechanical thrombectomy (MT) pose a treatment challenge. The aim of this study is to report our single center experience using the Neuroform Atlas stent as a potential rescue modality. Methods Data was analyzed from a prospectively maintained database at a Comprehensive Stroke Center between January 2019 and September 2021 on all ICAS-ELVO patients who underwent MT and required rescue stenting with the Neuroform Atlas. We systematically gathered demographic, clinical, procedural and functional characteristics on patients presenting with ELVO within 24 hours of last known normal. The primary outcome was the rate of revascularization following stenting. Results 26 patients met the inclusion criteria with a mean age of 56.5 years old, 34.6% of whom were female. On presentation the median National Institute of Health Stroke Scale (NIHSS) was 11 and media Alberta Stroke Program Early CT Score (ASPECTS) was 9. MT was performed using A Direct Aspiration First Pass Technique (ADAPT) in all patients. Following Neuroform Atlas stent placement, 3 patients (11.5%) had moderate in stent stenosis while severe stenosis was encountered in 4 patients (15.4%). The rate of successful revascularization (TICI IIB-III) was identified in 92.3% of the patients. On follow up vascular images, re-occlusion occurred in 2 patients (7.7%) and symptomatic hemorrhage was encountered in 3 patients (11.5%). Excellent outcome at 90 days (mRS 0–2) was achieved in 13/26 (50%) of patients. Conclusions Our series provides preliminary safety and efficacy data regarding the use of the Neuroform Atlas stent as a rescue modality in ICAS-ELVO cases. Disclosures O. Lajthia: None. E. Almallouhi: None. K. Kicielinski: None. J. Lena: None. A. Spiotta: None. S. Al Kasab: None.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".