Alberta Stroke Program Early CT Score applied to hyperdense lesion on noncontrast CT immediately post-thrombectomy is a predictor of poor outcome in acute ischemic stroke: A case-control study
Bibliographic record
Abstract
We aimed to evaluate whether Alberta Stroke Program Early CT Score (ASPECTS) applied to hyperdense lesion on noncontrast CT obtained immediately post-thrombectomy (post-ASPECTS) is useful for predicting poor outcome. We retrospectively reviewed patients who underwent noncontrast CT (NCCT) immediately after mechanical thrombectomy between January 2017 and July 2020 in our comprehensive stroke center. We collected baseline NCCT and post-ASPECTS score. The sensitivity, specificity, and positive and negative predictive values of the post-ASPECTS in predicting clinical outcome were calculated. A total of 223 patients were included. The hyperdense lesion on NCCT immediately after endovascular thrombectomy presented in 85.7% (191/223) patients, poor clinical outcome was in 56.1% (112/191) of hyperdense lesion patients. Low post-ASPECTS was associated with poor outcome (OR 0.390; 95% CI 0.258-0.589; P = .001), with an AUCROC curve of 0.753 (95% CI 0.684-0.822), while baseline NCCT-ASPECTS was not (OR 0. 754; 95% CI 0. 497-1.144; P = .185). A score ≤ 7 in post-ASPECTS was the best cut-off to poor clinical outcome (sensitivity 84.8%; specificity 52.7%; positive predictive value 68.4%; negative predictive value 73.8%). Our results point to the proportion of patients who present hyperdense lesion on NCCT is very high, post-ASPECTS could predict poor clinical outcomes in patients with stroke treated with endovascular mechanical thrombectomy, and post-ASPECTS may achieved better predictive value than baseline ASPECTS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".