Utility of the ASPECT Score for Predicting Intracranial Hemorrhage Following Intravenous Thrombolysis in Patients with Suspected MCA Infarction: Insights from the Northern Thai Stroke Registry
Bibliographic record
Abstract
Purpose: The association between the Alberta Stroke Programme Early CT Score (ASPECTS) and intracranial hemorrhage (ICH) in acute ischemic stroke (AIS) patients undergoing thrombolysis remains unclear. This study aimed to determine the relationship between ASPECTS and thrombolysis-associated outcomes, focusing on symptomatic (sICH) and asymptomatic (aICH) ICH. Patients and methods: AIS patients with middle cerebral artery (MCA) territory treated with thrombolysis were enrolled. Patients were categorized into favorable (8-10) and unfavorable (7 or less) ASPECTS. The primary outcomes were sICH and aICH. Secondary outcomes included ICH management, modified Rankin Scale (mRS), and mortality. Multivariable logistic regression analysis evaluated the risk of unfavorable ASPECTS and its association with study outcomes. Results: We included 622 patients (mean age 66.1 ± 13.5 years; 50.5% male); 95 (15.3%) had unfavorable ASPECTS. Patients with unfavorable ASPECTS had higher sICH but not aICH (21.1% vs 4.9%, P < 0.001 and 16.9% vs 17.3%, P = 1.00). Unfavorable ASPECTS was associated with sICH (adjusted odds ratio 5.1; 95% confidence interval 2.7-9.7, P < 0.001). Factors associated with lower ASPECTS included age ≥ 65 years, body weight < 60 kg, atrial fibrillation, onset-to-needle time ≥ 120 minutes, and anemia. Patients with lower ASPECTS had higher mortality and unfavorable mRS (>2) at discharge, 14 days, and 90 days (74.7% vs 50.1%, P < 0.001 for 90-day mRS >2). Conclusion: ASPECTS is a simple tool to predict thrombolysis-associated sICH but not aICH. Patients with unfavorable ASPECTS are at higher risk of complications and poor functional outcomes. Alternative treatments, such as mechanical thrombectomy, might be advisable for these patients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".