Optimizing Prediction Scores for Poor Outcome After Intra-Arterial Therapy in Anterior Circulation Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Intra-arterial therapy (IAT) promotes recanalization of large artery occlusions in acute ischemic stroke. Despite high recanalization rates, poor clinical outcomes are common. We attempted to optimize a score that combines clinical and imaging variables to more accurately predict poor outcome after IAT in anterior circulation occlusions. METHODS: Patients with acute ischemic stroke undergoing IAT at University of Texas (UT) Houston for large artery occlusions (middle cerebral artery or internal carotid artery) were reviewed. Independent predictors of poor outcome (modified Rankin Scale, 4-6) were studied. External validation was performed on IAT-treated patients at Emory University. RESULTS: A total of 163 patients were identified at UT Houston. Independent predictors of poor outcome (P≤0.2) were identified as score variables using sensitivity analysis and logistic regression. Houston Intra-Arterial Therapy 2 (HIAT2) score ranges 0 to 10: age (≤59=0, 60-79=2, ≥80 years=4), glucose (<150=0, ≥150=1), National Institute Health Stroke Scale (≤10=0, 11-20=1, ≥21=2), the Alberta Stroke Program Early CT Score (8-10=0, ≤7=3). Patients with HIAT2≥5 were more likely to have poor outcomes at discharge (odds ratio, 6.43; 95% confidence interval, 2.75-15.02; P<0.001). After adjusting for reperfusion (Thrombolysis in Cerebral Infarction score≥2b) and time from symptom onset to recanalization, HIAT2≥5 remained an independent predictor of poor outcome (odds ratio, 5.88; 95% confidence interval, 1.96-17.64; P=0.02). Results from the cohort of Emory (198 patients) were consistent; patients with HIAT2 score≥5 had 6× greater odds of poor outcome at discharge and at 90 days. HIAT2 outperformed other previously published predictive scores. CONCLUSIONS: The HIAT2 score, which combines clinical and imaging variables, performed better than all previous scores in predicting poor outcome after IAT for anterior circulation large artery occlusions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".