Collateral Circulation Predicts the Size of the Infarct Core and the Proportion of Salvageable Penumbra in Hyperacute Ischemic Stroke Patients Treated with Intravenous Thrombolysis
Bibliographic record
Abstract
BACKGROUND: We studied the impact of collateral circulation on CT perfusion (CTP) parametric maps and the amount of salvaged brain tissue, the imaging and clinical outcome at 24 h and at 3 months in a retrospective acute (<3 h) stroke cohort (105 patients) with anterior circulation thrombus treated with intravenous thrombolysis. METHODS: Baseline clinical and imaging information were collected and groups with different collateral scores (CS) were compared. Binary logistic regression analyses using good CS (CS ≥2) as the dependent variable were calculated. RESULTS: CTP Alberta Stroke Program Early CT Score (ASPECTS) was successfully assessed in 58 cases. Thirty patients displayed good CS. Poor CS were associated with more severe strokes according to National Institutes of Health Stroke Scale (NIHSS) at arrival (15 vs. 7, p = 0.005) and at 24 h (10 vs. 3, p = 0.003) after intravenous thrombolysis. Good CS were associated with a longer mean onset-to-treatment time (141 vs. 121 min, p = 0.009) and time to CTP (102 vs. 87 min, p = 0.047), better cerebral blood volume (CBV) ASPECTS (9 vs. 6, p < 0.001), better mean transit time (MTT) ASPECTS (6 vs. 3, p < 0.001), better noncontrast CT (NCCT) ASPECTS (10 vs. 8, p < 0.001) at arrival and with favorable clinical outcome at 3 months (modified Rankin Scale ≤2, p = 0.002). The fraction of penumbra that was salvageable at arrival and salvaged at 24 h was higher with better CS (p < 0.001 and p = 0.035, respectively). In multivariate analysis, time from the onset of symptoms to imaging (p = 0.037, OR 1.04 per minute, 95% CI 1.00-1.08) and CBV ASPECTS (p = 0.001, OR 2.11 per ASPECTS point, 95% CI 1.33-3.34) predicted good CS. In similar multivariable models, MTT ASPECTS (p = 0.04, OR 1.46 per ASPECTS point, 95% CI 1.02-2.10) and NCCT ASPECTS predicted good CS (p = 0.003, OR 4.38 per CT ASPECTS point, 95% CI 1.66-11.55) along with longer time from the onset of symptoms to imaging (p = 0.045, OR 1.03 per minute, 95% CI 1.00-1.06 and p = 0.02, OR 1.05 per minute, 95% CI 1.00-1.09, respectively). CBV ASPECTS had a larger area under the receiver operating characteristic curve for good CS (0.837) than NCCT ASPECTS (0.802) or MTT ASPECTS (0.752) at arrival. CONCLUSIONS: Favorable CBV ASPECTS, NCCT ASPECTS and MTT ASPECTS are associated with good CS along with more salvageable tissue and longer time from the onset of symptoms to imaging in ischemic stroke patients treated with intravenous thrombolysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".