Preliminary application of CT perfusion source images for evaluating regional collateral circulation in unilateral Moyamoya disease
Bibliographic record
Abstract
BACKGROUND: Collateral flow is associated with clinical outcomes for patients with Moyamoya disease and served as a parameter for patient selection of therapeutic strategies. Therefore, we explored whether a noninvasive imaging modality, computed tomography perfusion (CTP) source images (CTP-Sis), could be used to identify the presence and intensity of collateral flow using digital subtraction angiography (DSA) as a gold standard for collateral flow. METHODS: CTP-Sis and DSA were performed for 24 patients with unilateral Moyamoya disease. A collateral grading system was developed based on arterial and venous phase CTP-Sis, imitating the DSA score system. Two neuroradiologists scored the DSA images using a collateral grading scale for the regions of interest corresponding to the Alberta Stroke Program Early computed tomography Score (ASPECTS) methodology. Another two neuroradiologists scored CTP-Sis in a similar manner. Agreement between the CTP-Sis and DSA consensus scores was determined, including kappa statistics. RESULTS: The agreement between the CTP-Sis and DSA consensus readings was moderate to strong, with a weighted kappa value of 0.768 [95% confidence interval (CI), 0.703-0.832], but there was a better agreement for readers of CTP-Sis, as compared with those of DSA. The sensitivity and specificity for identifying collaterals with CTP-Sis were 0.714 (95% CI, 0.578-0.851) and 0.995 (95% CI, 0.985-1.000), respectively. CONCLUSIONS: CTP-Sis could help identify in a noninvasive manner the presence and intensity of collateral flow in patients with unilateral Moyamoya disease using DSA as a gold standard. Further study with a large number of cases is warranted. Further application of this method to other cerebrovascular diseases including acute ischemic stroke can also be warranted.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".