E-065 Experience with neuroform atlas stenting as rescue endovascular treatment after failed mechanical thrombectomy secondary to intracranial atherosclerosis
Bibliographic record
Abstract
<h3>Background</h3> 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. <h3>Methods</h3> 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. <h3>Results</h3> 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. <h3>Conclusions</h3> Our series provides preliminary safety and efficacy data regarding the use of the Neuroform Atlas stent as a rescue modality in ICAS-ELVO cases. <h3>Disclosures</h3> <b>O. Lajthia:</b> None. <b>E. Almallouhi:</b> None. <b>K. Kicielinski:</b> None. <b>J. Lena:</b> None. <b>A. Spiotta:</b> None. <b>S. Al Kasab:</b> None.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".