Total and regional ASPECT score for non-contrast CT, CT angiography, and CT perfusion: inter-rater agreement and its association with the final infarction in acute ischemic stroke patients
Bibliographic record
Abstract
Background Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a grading system to assess the extent and distribution of early ischemic changes. Purpose To assess inter-rater agreement for total and regional ASPECTS on non-contrast computed tomography (NCCT) images, CT angiography source images (CTA-SI), and CT-perfusion cerebral blood volume (CTP-CBV) maps, and their association with final infarction in patients with acute ischemic stroke (AIS). Material and Methods A total of 96 consecutive patients with AIS who underwent pre-treatment NCCT and CTP were retrospectively enrolled. CTA-SI was reconstructed using the raw data of CTP. Total and regional ASPECTS were assessed on baseline NCCT, CTA-SI, and CTP-CBV, and on follow-up NCCT or diffusion-weighted imaging. Follow-up ASPECTS served as the reference standard for final infarction. Results CTP-CBV demonstrated higher concordance for total ASPECTS (interclass correlation coefficient, 0.895 vs. 0.771 vs. 0.777) and regional ASPECTS in internal capsule, lentiform, caudate nuclei, M5 and M6, compared with NCCT and CTA-SI. CTP-CBV showed a trend of stronger correlation with final ASPECTS than NCCT and CTA-SI (0.717 vs. 0.711 vs. 0.565; P > 0.05). ASPECTS in the internal capsule (ρ, 0.756 vs. 0.556; P = 0.016) and caudate nucleus (ρ, 0.717 vs. 0.476; P = 0.010) on CTP-CBV were more strongly correlated with follow-up ASPECTS than NCCT. CTP-CBV showed higher accuracy for predicting final infarction in the internal capsule (92.5% vs. 90.3% and 87.1%; P > 1.000, P = 0.125, respectively) and caudate nucleus (87.1% vs. 79.6% and 77.4%; P = 0.453, P = 0.039, respectively) than CTA-SI and NCCT. Conclusion CTP-CBV ASPECTS might be more reliable for delineating early ischemic changes and predicting final infarction.
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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 | 0.004 | 0.012 |
| 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 | 0.001 |
| 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".