Selection of Acute Ischemic Stroke Patients for Intra-Arterial Thrombolysis With Pro-Urokinase by Using ASPECTS
Bibliographic record
Abstract
BACKGROUND: Previous studies have suggested that baseline computed tomographic (CT) scans might be a useful tool for selecting particular ischemic stroke patients who would benefit from thrombolysis. The aim of the present study was to assess whether the baseline CT scan, assessed with the Alberta Stroke Program Early CT Score (ASPECTS), could identify ischemic stroke patients who might particularly benefit from intra-arterial thrombolysis of middle cerebral artery occlusion. METHODS: Baseline and 24-hour follow-up CT scans of patients randomized within 6 hours of symptoms to intra-arterial thrombolysis with recombinant pro-urokinase or control in the PROACT-II study were retrospectively scored by using ASPECTS. Patients were stratified into those with ASPECTS >7 or < or =7. Independent functional outcome at 90 days was compared between the 2 strata according to treatment assignment. RESULTS: The analysis included 154 patients with angiographically confirmed middle cerebral artery occlusion. The unadjusted risk ratio of an independent functional outcome, in favor of treatment, in the ASPECTS >7 group was 5.0 (95% confidence interval [CI], 1.3 to 19.2) compared with 1.0 (95% CI, 0.6 to 1.9) in the ASPECTS < or =7 group. After adjustment for baseline characteristics, the risk ratio in the ASPECTS score >7 was 3.2 (95% CI, 1.2 to 9.1). Similar favorable treatment effects were observed when secondary outcomes were used, but these did not reach statistical significance. CONCLUSIONS: Ischemic stroke patients with a baseline ASPECTS >7 were 3 times more likely to have an independent functional outcome with thrombolytic treatment compared with control. Patients with a baseline ASPECTS < or =7 were less likely to benefit from treatment. This observation suggests that ASPECTS can be both a useful clinical tool and an important method of baseline risk stratification in future clinical trials of acute stroke therapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".