Hemodynamic Alterations in Vertebrobasilar Large Artery Disease Assessed by Arterial Spin-Labeling MR Imaging
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: VB artery stenosis is associated with a high risk of recurrent ischemic events, and knowledge about the hemodynamic relevance of VB stenosis is important for clinical decision making. In this study, multiple inflow pulsed ASL MR imaging was assessed for its ability to measure CBF and ATT in patients with VB disease. MATERIALS AND METHODS: ASL was performed on a 3T MR imaging scanner in 41 participants. Twenty-one patients had a history of ischemic events in the VB circulation (14 men, 7 women, age 66 ± 11 years). Clinical data and CE-MRA were used to classify VB disease severity. Twenty age-matched adults were controls. Group and within-VB analyses were performed. Mean CBF and ATT values in the ROIs were adjusted by excluding voxels that did not produce a reliable ASL estimate. RESULTS: CBF was reduced (P < .003) in patients compared with controls, which was significant after excluding voxels with a poor fit. Differences in ATT between patients and controls were not significant after voxel correction. There was a strong correlation between CBF and ATT among patients. Finally, ATT was significantly correlated with VB disease severity (P = .026). CONCLUSIONS: Multiple inflow ASL distinguished patients with VB disease from age matched-controls. VB disease rating was associated with prolonged ATT downstream. ASL may have diagnostic potential among patients in whom risk of intervention is high.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".