Reliability, Reproducibility and Prognostic Accuracy of the Alberta Stroke Program Early CT Score on CT Perfusion and Non-Contrast CT in Hyperacute Stroke
Bibliographic record
Abstract
BACKGROUND: Alberta Stroke Program Early CT Score (ASPECTS) assesses early ischemic change on non-contrast CT (NCCT). We hypothesised that assessing ASPECTS regions on CT Perfusion (CTP) rather than NCCT would improve inter-rater agreement and prognostic accuracy, particularly in patients presenting early after stroke onset. METHODS: Ischemic stroke patients treated with intravenous alteplase from 2009 to 2014 at our institution were included in this study. Inter-rater agreement and prognostic accuracy of ASPECTS across modalities were analysed by the time between stroke onset and initial NCCT, dichotomized 1st quartile versus quartiles 2-4, referred to as epochs. ASPECTS was assessed by 2 independent raters, blinded to stroke onset time, with agreement determined by weighted kappa (κw). Prognostic accuracy for favourable outcome (modified Rankin Scale 0-2) was assessed using the receiver-operating characteristic analysis. RESULTS: A total of 227 participants were included. There was significant time-by-CT modality interaction for ASPECTS, p < 0.0001. The inter-rater agreement of ASPECTS on NCCT significantly increased as onset to CT time increased (κw epoch 1 = 0.76 vs. κw epoch 2-4 = 0.89, p = 0.04), whereas agreement using CTP parameters was stable across epochs. Inter-rater agreement for CTP-ASPECTS was significantly higher than NCCT in early epoch: Tmax κw = 0.96, p = 0.002; cerebral blood volume (CBV) κw = 0.95, p = 0.003; cerebral blood flow (CBF) κw = 0.94, p = 0.006, with no differences in the later epochs. Prognostic accuracy of ASPECTS on NCCT in epoch 1 were (area under the ROC curves [AUC] = 0.52, 95% CI 0.48-0.56), CBV (AUC = 0.55, 95% CI 0.42-0.69, CBF (AUC = 0.58, 95% CI 0.46-0.71) and Tmax (AUC = 0.62, 95% CI 0.49-0.75), p = 0.46 between modalities. CONCLUSIONS: CTP can improve reliability when assessing the extent of ischemic changes, particularly in patients imaged early after stroke onset.
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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 | 0.023 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".