ORIGINAL RESEARCH Identification of Infarct Core and Penumbra in Acute Stroke Using CT Perfusion Source Images
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: CT perfusion (CTP) mapping has been reported to be useful in the differentiation of the infarct core and ischemic penumbra. However, the value of the CTP source imaging (CTP-SI) during the arterial and venous phases has not been fully investigated. The purpose of this study was to develop a CTP-SI methodology for acute ischemic stroke and compare its efficacy with cerebral blood flow (CBF) and cerebral blood volume (CBV) in predicting infarct core and penumbra. MATERIALS AND METHODS: CT examinations, including non-contrast-enhanced CT, CTP, and CT angiography (CTA), were performed in 42 patients with symptoms of stroke for 9 hours. The Alberta Stroke Program Early CT Score (ASPECTS) was analyzed on the arterial phase CTP-SI and venous phase CTP-SI and then compared with the ASPECTS on CBF and CBV for efficacy assessment. RESULTS: The ASPECTS on the arterial phase CTP-SI was closely correlated with the ASPECTS on CBF, the Pearson correlation coefficient was 0.88 (P .001), and the concordance correlation coefficient was 0.7603 (95 % confidence interval [CI], 0.6331–0.8476). The ASPECTS on the venous phase CTP-SI revealed a significant correlation with the ASPECTS on CBV, the Pearson correlation coefficient was 0.92 (P .001), and the concordance correlation coefficient was 0.8880 (95 % CI,
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.003 | 0.001 |
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".