Abstract P545: First Pass TICI 2b versus Multiple Passes TICI 3 for Acute Ischemic Stroke Patients: Is First Pass TICI 2b Enough?
Bibliographic record
Abstract
Background and Objective: Prompt and complete reperfusion with mechanical thrombectomy (MT) is essential to improve outcome in acute ischemic strokes (AIS) with large vessel occlusion (LVO). Recently, first-pass effect (FPE), defined as achieving complete reperfusion with a single pass, has been emphasized as a potentially important MT target. We aimed to compare outcomes between patients who achieve mTICI 2b with first pass to those with multiple devise passes (MDP) mTICI 3. Methods: From a single comprehensive stroke center database, we retrospectively grouped LVO pts treated with MT into those who achieved mTICI 2b after a single pass and mTICI 3 after MDP. Clinical outcome (discharge and 90-day mRS), discharge NIHSS and safety (sICH, neurological worsening, mortality) were compared between the two groups. Results: Of 186 pts included, 153 (82%) achieved mTICI 3 with MDP, and 33 (18%) had mTICI 2b after a single pass. Mean age (71 vs 69), NIHSS (17 vs 16, p=0.2) were similar between the two groups. Patients with a single pass mTICI 2b had numerically higher IV tPA administration (33% vs 46%, p=.16). There was no difference in other baseline characteristics. There was no significant difference in discharge (21% vs 24.2%, p=0.65) and 90-day mRS 0-2 (24% vs 24%, p=0.5), MDP mTICI 3 and single pass mTICI 2b, respectively. Also, there was no difference in discharge NIHSS score (13.6 vs 16.7, p=0.26), mortality (16.3% vs 18.2%, p=0.8) and sICH rates (7.8% vs 18.2%, p=0.095) or neurological worsening (76.5% vs 69.7%, p=1). Conclusion: Our results did not show a significant difference between mTICI 3 with multiple passes and mTICI 2b after a single pass. Future large studies are warranted to explore the possibility of extending the first pass effect to patients who achieve mTICI 2b with a single pass.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".