CT perfusion extended window ischemic core estimation: Bayesian algorithm versus oscillation index singular value decomposition
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Ischemic core estimation by CT perfusion (CTp) is a diagnostic challenge, mainly because of the intrinsic noise associated with perfusion data. However, an accurate and reliable quantification of the ischemic core is critical in the selection of patients for reperfusion therapies. Our study aimed at assessing the diagnostic accuracy of two different CTp postprocessing algorithms, that is, the Bayesian Method and the oscillation index singular value decomposition (oSVD). METHODS: All the consecutive stroke patients studied in the extended time window (>4.5 hours from stroke onset) by CTp and diffusion-weighted imaging (DWI), between October 2019 and December 2021, were enrolled. The agreement between both algorithms and DWI was assessed by the Bland-Altman plot, Wilcoxon signed-rank test, Spearman's rank correlation coefficient, and the intraclass correlation coefficient (ICC). RESULTS: Twenty-four patients were enrolled (average age: 72 ± 15 years). The average National Institutes of Health Stroke Scale was 14.42 ± 6.75, the median Alberta Stroke Program Early CT score was 8.50 (interquartile range [IQR] = 7.75-9), and median time from stroke onset to neuroimaging was 7.5 hours (IQR = 6.5-8). There was an excellent correlation between DWI and oSVD (ρ = .87, p-value < .001) and DWI and Bayesian algorithm (ρ = .94, p-value < .001). There was a stronger ICC between DWI and Bayesian algorithm (.97, 95% confidence interval [CI]: .92-.99, p-value < .001) than between DWI and oSVD (.59, 95% CI: .26-.8, p-value < .001). DISCUSSION: The agreement between Bayesian algorithm and DWI was greater than between oSVD and DWI in the extended window. The more accurate estimation of the ischemic core offered by the Bayesian algorithm may well play a critical role in the accurate selection of patients for reperfusion therapies.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".