Abstract WP227: Noninvasive Blood Flow Measures in Atherosclerosis of the Posterior Circulation: Quantitative MRA Bests TOF Signal Intensity Ratio in VERiTAS
Bibliographic record
Abstract
Background: Fractional flow across an atherosclerotic lesion measured with TOF-MRA signal intensity ratio (SIR) may be used to gauge hemodynamic severity and to predict subsequent stroke. The degree of flow impairment may also be ascertained by quantitative MRA (QMRA). We analyzed performance of these noninvasive imaging parameters to estimate risk of subsequent posterior circulation events in VERiTAS. Methods: TOF-MRA data and QMRA were simultaneously acquired in VERiTAS. SIR were derived from TOF source images and normalized for analysis with volume flow ratios (VFR) on QMRA at standard anatomical landmarks and across the maximal stenosis. Statistics analyzed the correlation between SIR and VFR, and the ability of each to predict clinical events. Results: 72 subjects (mean age 65.6±10.3 years, 32 (44%) women) with posterior circulation atherosclerosis were enrolled in VERiTAS. Posterior communicating artery (PCOMM) flow to the posterior circulation was detected in 85% on the right, in 86% on the left, with bilateral PCOMM flow in 78%. Fractional flow measures or SIR across the maximal stenotic lesion evident on TOF MRA was reduced in 43%, increased in 16%, with no change in 40%. SIR from the proximal to distal basilar artery segments increased in 62%, was unchanged in 33% and decreased in 4% of cases. SI and VFR exhibited limited correlation at corresponding arterial segments. QMRA VFR indicative of low distal flow status predicted subsequent clinical events, unlike SIR. Conclusions: Evaluation of hemodynamics in posterior circulation atherosclerosis reveals superiority of QMRA to SIR in prospectively predicting recurrent ischemia. Collateral circulation, tandem disease and unique aspects of vertebrobasilar atherosclerosis likely influence the utility of SIR on TOF MRA.
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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 | 0.002 |
| 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) | 0.003 | 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".