Thrombectomy in Stroke Patients With Low Alberta Stroke Program Early Computed Tomography Score: Is Modified Thrombolysis in Cerebral Infarction (mTICI) 2c/3 Superior to mTICI 2b?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Outcomes following mechanical thrombectomy (MT) are strongly correlated with successful recanalization, traditionally defined as modified Thrombolysis in Cerebral Infarction (mTICI) ≥2b. This retrospective cohort study aimed to compare the outcomes of patients with low Alberta Stroke Program Early Computed Tomography Score (ASPECTS; 2-5) who achieved mTICI 2b versus those who achieved mTICI 2c/3 after MT. METHODS: This study utilized data from the Stroke Thrombectomy and Aneurysm Registry (STAR), which combined databases from 32 thrombectomy-capable stroke centers between 2013 and 2023. The study included only patients with low ASPECTS who achieved mTICI 2b, 2c, or 3 after MT for internal carotid artery or middle cerebral artery (M1) stroke. RESULTS: Of the 10,229 patients who underwent MT, 234 met the inclusion criteria. Of those, 98 (41.9%) achieved mTICI 2b, and 136 (58.1%) achieved mTICI 2c/3. There were no significant differences in baseline characteristics between the two groups. The 90-day favorable outcome (modified Rankin Scale score: 0-3) was significantly better in the mTICI 2c/3 group than in the mTICI 2b group (adjusted odds ratio 2.35; 95% confidence interval [CI] 1.18-4.81; P=0.02). Binomial logistic regression revealed that achieving mTICI 2c/3 was significantly associated with higher odds of a favorable 90-day outcome (odds ratio 2.14; 95% CI 1.07-4.41; P=0.04). CONCLUSION: In patients with low ASPECTS, achieving an mTICI 2c/3 score after MT is associated with a more favorable 90-day outcome. These findings suggest that mTICI 2c/3 is a better target for MT than mTICI 2b in patients with low ASPECTS.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".