Abstract WMP42: Hypoperfusion Symptoms Perform Poorly as an Indicator of Hemodynamic Compromise in Vertebrobasilar Disease: Results From the VERiTAS Study
Bibliographic record
Abstract
Introduction: Hypoperfusion symptoms, defined as symptoms related to change in position (i.e. supine to seated), effort or exertion, or recent change in antihypertensive medication have been used in stroke studies as a surrogate for detecting hemodynamic compromise. However, the validity of these symptoms in identifying flow compromise in patients has not been well established. We examined whether hypoperfusion symptoms correlated with quantitative evaluation of flow compromise in the prospective observational Vertebrobasilar Flow Evaluation and Risk of Transient Ischemic Attack and Stroke (VERiTAS) study. Methods: VERiTAS enrolled patients with recent vertebrobasilar TIA or stroke and ≥50% atherosclerotic stenosis or occlusion in vertebral and/or basilar arteries. Hemodynamic status using large vessel flow in the vertebrobasilar territory was measured using quantitative magnetic resonance angiography (QMRA), and patients were designated as low, borderline or normal flow based on distal territory regional flow, incorporating collateral capacity, as previously reported. The presence of qualifying event hypoperfusion symptoms was assessed relative to the quantitatively determined flow status (normal vs borderline/low), and also examined as a predictor of subsequent stroke risk. Results: Of the 72 enrolled subjects, 66 had data on hypoperfusion symptoms available. On initial QMRA designation, 43 subjects were designated as normal flow vs. 23 subjects designated as low flow (n=16) or borderline (n=7). Of these, 5 (11.6%) normal flow and 3 (13.0%) low/borderline flow subjects reported at least one qualifying event hypoperfusion symptom (p=0.99, Fisher’s exact test). Hypoperfusion symptoms had a positive predictive value of 37.5% and negative predictive value of 65.5% for low/borderline flow status. Compared to flow status, which strongly predicted subsequent stroke risk, hypoperfusion symptoms were not associated with stroke outcome (p=0.87, log rank test). Conclusions: These results suggest that hypoperfusion symptoms alone correlate poorly with actual hemodynamic compromise, and subsequent stroke risk in vertebrobasilar disease, and are not a reliable surrogate for flow measurement.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".