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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".