Vertebrobasilar Flow Evaluation and Risk of Transient Ischaemic Attack and Stroke Study (Veritas): Rationale and Design
Bibliographic record
Abstract
BACKGROUND: Over one-third of ischaemic strokes occur in the posterior circulation, and a leading cause is atherosclerotic vertebrobasilar disease. Symptomatic vertebrobasilar disease carries a high annual recurrent stroke risk, averaging 10-15% per year. Endovascular angioplasty and stenting are increasingly used but carry risks, and the benefit remains unproven. Determining stroke predictors in this population is critical to identifying high-risk patients for future trials of intervention. Preliminary studies indicate that stroke risk in vertebrobasilar disease is strongly related to haemodynamic compromise, which can be measured noninvasively using quantitative magnetic resonance angiography. METHODS/STUDY DESIGN: The Vertebrobasilar Flow Evaluation and Risk of Transient Ischaemic Attack and Stroke (VERiTAS) study, a prospective multicentre NIH-funded observational study of symptomatic vertebrobasilar stenosis (≥50%) or occlusion, is designed to test the hypothesis that patients demonstrating compromised blood flow as assessed by quantitative magnetic resonance angiography are at higher stroke risk. The study will recruit 80 patients at six sites in North America over 4-years. Upon enrollment, subjects will undergo haemodynamic assessment with blinded quantitative magnetic resonance angiography to assess large vessel flow in the vertebrobasilar territory, and be prospectively designated as compromised or normal flow. Patients will be re-imaged with quantitative magnetic resonance angiography at 6-, 12-, and 24-months, and followed for 12-24-months for the primary end-point of stroke in the vertebrobasilar territory. CONCLUSION: The VERiTAS study is the first prospective study of haemodynamics and stroke risk in the posterior circulation. The results may impact the selection criteria for interventional candidates and also define a low-risk population in whom the risks of invasive interventions would be unnecessary.
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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 | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".