Baseline ASPECTS and hypoperfusion intensity ratio influence the impact of first pass reperfusion on functional outcomes
Bibliographic record
Abstract
BACKGROUND: First pass reperfusion (FPR) has been established as a key performance metric in mechanical thrombectomy (MT). The impact of FPR may be more relevant in fast progressors. We aim to study the impact of baseline Alberta Stroke Program Early CT Score (ASPECTS) on non-contrast CT and hypoperfusion intensity ratio (HIR) on CT perfusion on clinical outcomes after FPR. METHODS: A prospective MT database was reviewed for patients with isolated occlusion of the intracranial internal carotid artery and/or middle cerebral artery M1 segment who underwent MT with complete reperfusion (modified Thrombolyis in Cerebral Infarction score 2c-3) from January 2012 to May 2019. The overall population was divided into ASPECTS >7 versus ≤7 and the subgroup of patients with baseline CT perfusion was divided into HIR <0.3 versus ≥0.3. Univariable and multivariable analyses were performed to establish the predictors of 90-day functional independence (modified Rankin Scale (mRS) ≤2) in each subgroup. RESULTS: A total of 436 patients were included in the analyses. FPR was achieved in 254 (58.3%) patients. ASPECTS modified the effect of FPR on clinical outcomes, with FPR predicting good outcomes in patients with ASPECTS ≤7 (46% vs 29%, adjusted OR 3.748; 95% CI 1.590 to 8.838, p=0.003) while no significant effect was detected in those with ASPECTS >7 (62.3% vs 53.1%, adjusted OR 1.372; 95% CI 0.798 to 2.358, p=0.25). Similarly, FPR predicted good outcomes in patients with HIR ≥0.3 (54.8% vs 41.9%, adjusted OR 2.204; 95% CI 1.148 to 4.233, p=0.01) but not in those with HIR <0.3 (62.9% vs 52.8%, adjusted OR 1.524; 95% CI 0.592 to 3.920, p=0.38). CONCLUSIONS: The impact of FPR on functional outcomes is highly dependent on baseline imaging characteristics, with a more prominent influence in patients presenting with lower ASPECTS and/or higher HIR.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".