Prognosis with non-contrast CT and CT Perfusion imaging in thrombolysis-treated acute ischemic stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Alberta Stroke Program Early CT Score (ASPECTS) and hyperdense vessel sign (HDVS) on baseline non-contrast CT (NCCT) may benefit prognosis of acute ischemic stroke (AIS). We aimed to investigate the agreement of ASPECTS between automated and manual interpretations, and further understand the roles of NCCT and CT Perfusion (CTP) in prognosis. MATERIALS AND METHODS: From January 2019 to May 2020, thrombolysis-treated AIS patients undergoing NCCT and Perfusion imaging before treatment were retrospectively reviewed. A radiologist, a senior neuroradiologist and a neurologist blindly interpreted ASPECTS from NCCT images and a prototypical software produced automated results. Another independent radiologist determined presence of HDVS and CTP-ASPECTS. Three-month modified Rankin scale (mRS) ≤ 2 indicated good functional outcome. NCCT ASPECTS were compared against CTP-ASPECTS using squared weighted kappa. Univariable, multivariable and receiver operating characteristics (ROC) analysis were conducted to evaluate the prognostic value of clinical risk factors, NCCT and CTP findings. RESULTS: Seventy-five patients were included in this study, of whom 35 (46.7%) presented favorable outcome. Fair to substantial agreement with CTP-ASPECTS was witnessed for automated and manual interpretations (0.685, automated; 0.778, radiologist; 0.830, neuroradiologist; 0.313, neurologist). ASPECTS, HDVS, infarct core volume and mismatch ratio were univariably related to functional outcome, and infarct core volume remained as an independent prognostic factor in the multivariable analysis. The multivariable model achieved an area under ROC (AUC) of 0.768 (95% CI, 0.666-0.870). CONCLUSIONS: Automated ASPECTS achieves substantial agreement with reference CTP-ASPECTS, and comprehensive CT assessment may benefit AIS prognosis after intravenous thrombolysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".