Application of a new mismatch model on evaluating infarct core and penumbra in acute ischemic stroke using CT perfusion source images
Bibliographic record
Abstract
Objective To assess the diagnostic value of determining infarct core and penumbra using CT perfusion source images (CTP-SI) mismatch model in hemispheric stroke less than 9 hours.Methods one-stop shop CT examination including non-contrast enhanced CT (NCCT), CTP, CT angiography (CTA) were performed in 24 patients with symptoms of stroke less than 9 hours.The Alberta Stroke Program Early CT Score (ASPECTS) were analyzed on arterial phase CTP-SI and venous phase CTP SI using Wilcoxon rank-sum test, then compared with the follow up imaging ASPECTS using multiple linear regression.Results The median (min-max) scores of ASPECTS on arterial phase CTP-SI, venous phase CTP-SI and follow-up imaging were 9.0 ( 2.0-10.0 ), 9.3 ( 6.5-10.0 ) and 9.0 ( 7.0-10.0 ),respectively. ASPECTS measured on arterial phase CTP-SI significantly differed from the ASPECTS on venous phase CTP-SI ( Z =-2.812, P = 0.005 ).Moreover, the linear regression analysis showed significant correlation between the ASPECTS on venous phase CTP-SI and follow up imaging ASPECTS ( Beta =0.715,P = 0.003 ).Conclusion CTP-SI mismatch model provides a method of choice in predicting penumbra and infarct core in hemispheric stroke. Key words: Cerebrovascular accident; Tomography,X-ray computed; Perfusion
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".