E-208 Outcomes of mechanical thrombectomy for medium vessel occlusion in acute ischemic stroke patients with aspects 4–5 vs. 6–7: a retrospective, multicenter, and multinational study
Bibliographic record
Abstract
Background Mechanical thrombectomy (MT) has revolutionized the treatment of acute ischemic stroke (AIS) with large vessel occlusion (LVO). However, its efficacy in medium vessel occlusion (MeVO) stroke, particularly in patients with low Alberta Stroke Program Early Computed Tomography Score (ASPECTS), remains less explored. Methods This retrospective study analyzed data from 443 AIS patients treated with MT for MeVO and low ASPECTS (4–7) at 37 centers across North America, Asia, and Europe, from September 2017 to July 2021. Patients were categorized into ASPECTS of 4–5 and 6–7. Baseline characteristics, procedural details, and clinical outcomes were assessed. Results Of 443 patients, 51 (12%) had ASPECTS of 4–5, and 392 (88%) had scores of 6–7. The median age was 65 years (IQR: 46–79), with a balanced sex distribution between the groups. The most common site of initial occlusion was M2 branch in both groups (92% in ASPECTS 4–5 and 85% in ASPECTS 6–7) (p=0.68). The ASPECTS 4–5 group had lower TICI 2c-3 achievement (31% vs. 55%, p=0.002) and poorer functional outcomes (mRS 0–1 at 90 days: 12% vs. 29%, p=0.03) compared to the ASPECTS 6–7 group. Intracranial hemorrhagic complications were higher in the ASPECTS 4–5 group (69% vs. 47%, p=0.007). Multivariable analysis revealed ASPECTS 6–7 to be associated with higher odds of TICI 2c-3 (OR: 2.5; CI: 1.28 to 4.89, p=0.007) and lower odds of intracranial hemorrhagic complications (OR: 0.4; CI: 0.19 to 0.81, p=0.012). Conclusion MT was associated with higher rates of hemorrhagic complications and less favorable functional outcomes in patients with very low ASPECTS (4–5) as compared to those with moderate-to-low ASPECTS (5–6). These findings highlight the importance of patient selection based on ASPECTS for MT in MeVO. Disclosures B. Musmar: None. N. Adeeb: None. H. Salim: None. S. Ghozy: None. A. Guenego: None. N. M Cancelliere: None. A. A Dmytriw: None. V. Mendes Pereira: None. P. Jabbour: 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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".