COMPARISON OF THE ASPECT SCORING SYSTEM ON NONCONTRAST CT AND ON BRAIN CT ANGIOGRAPHY IN ISCHEMIC STROKE.
Bibliographic record
Abstract
The ASPECTs scoring system has been used to prognosticate, for example the score is a strong predictor of functional outcome in acute anterior circulation ischemic stroke. The effectiveness of thrombolysis and thrombectomy in patients with middle cerebral artery occlusion shows effect modification by the Alberta Stroke Program Early CT Score. We compared the ASPECTs scoring system on noncontrast CT and on brain CT angiography (arterial phase-ASPECTAs) for predicting functional outcomes in acute anterior circulation ischemic stroke. 81 consecutive patients with acute anterior circulation ischemic stroke treated with thrombectomy during 2019-2020 were included. Two independent radiologists evaluated score by using the Alberta Stroke Program Early CT methodology on NCCT and CTA. Good and extremely poor outcomes at 3 months were defined by modified Rankin Scale scores of 0-2 and 5-6 points, respectively. Factors associated with outcome on univariable analysis were ASPECT, ASPECTAS, lower NIHSS scores, and time to recanalization. On multivariable logistic regression ASPECTAS ≥5 were independent predictors of good outcomes. ASPECTs scoring system on brain CT angiography (arterial ASPECTs) is superior than non contrast ASPECT for predicting of good outcome in pacients with acute anterior circulation ischemic stroke.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".