Abstract P473: Perfusion Imaging Identifies Patients With Mild Deficits Due to Large Vessel Occlusion Who May Benefit From Endovascular Thrombectomy: A Pooled International Cohort Study
Bibliographic record
Abstract
Background: Efficacy and safety of endovascular thrombectomy (EVT) in large vessel occlusion (LVO) patients with mild deficits is unclear. Methods: Pooled cohort of pts with mild deficits (NIHSS<6) due to (ICA, M1, M2) LVO from EXTEND IA TNK I & II RCTs and prospective data from 12 centers (US, AUS, NZ, Canada, Spain) from 1/2013 to 2/2020 was divided into medical management (MM) vs EVT. All pts had baseline CT, CTA, CTPRAPID software estimated ischemic core and mismatch. Pts stratified into with or without target profile (≥1cc core / mismatch ratio ≥ 1.8 / mismatch volume ≥ 15cc). Primary outcome- excellent (90 day mRS 0-1); Secondary- mRS shift, safety (sICH, neuro-worsening, mortality). Results: Of 371 pts, 189 (51%) had EVT. Time LKW to EVT center: EVT 165 (70- 416) vs MM 200 (72-564) min, p=0.35 were similar. EVT pts had larger perfusion lesions (51 cc (23-86) vs 30.1 (5, 65), p<0.001), higher NIHSS 4 (2-5) vs 3 (2-4), p=0.009), less IV tPA (30% vs 41%, p=0.044), more M1s (44% vs 29%, p<0.001). 93 pts (25%) had target profile, of whom 60% had EVT. Of 278 without target profile, 48% had EVT. Among all pts, excellent outcomes and mRS distribution were similar (EVT 63.5% vs MM 59.1%, aOR 1.55, 95%, p=0.16) and (adj cOR 1.44, p=0.16) Fig 1A. EVT had worse safety; sICH (6% vs 0%, p=0.002); neuro-worsening (19% vs 3%, p<0.001) and mortality (5% vs 1%, p=0.06). With target profile, EVT associated with more excellent outcomes (66% vs 49%, aOR 4.44, 95% CI 1.04-18.95, p=0.04), shift to better outcomes (adj cOR 2.9, 95% CI 1.03-7.91, p=0.04) Fig 1B. Safety was similar; sICH 2% vs 0%, p>0.99, neuro-worsening 17% vs 6%, p=0.30) and mortality 5% vs 3%, p>0.99). Without target profile, excellent outcomes were similar without a shift, Fig 1C. Safety was worse with EVT: sICH 8% vs 0%, p=0.001; neuro-worsening 20% vs 3%, p<0.001). Conclusion: EVT was not associated with improved outcomes in patients with mild deficits; safety was worse. However, EVT was safe and associated with improved outcomes in target profile patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".