Infarct Patterns in Patients with Atherosclerotic Vertebrobasilar Disease in Relation to Hemodynamics
Bibliographic record
Abstract
INTRODUCTION: Distal territory blood flow is independently associated with subsequent strokes in symptomatic vertebrobasilar atherosclerotic disease. We aimed to assess infarct patterns in relation to hemodynamic status in the prospective Vertebrobasilar Flow Evaluation and Risk of Transient Ischemic Attack and Stroke (VERiTAS) study. METHODS: Distal territory blood flow was measured using quantitative magnetic resonance angiography (MRA) in 72 patients with symptomatic atherosclerotic vertebrobasilar disease, and then dichotomized into normal (n = 54) and low (n = 18) flow. Patients were followed longitudinally on standard medical management. Two observers blinded to flow status independently reviewed the imaging performed at the time of subsequent strokes, in order to adjudicate the likely mechanism based on infarct patterns. The frequency of stroke mechanisms was qualitatively compared based on flow status. RESULTS: During a median follow-up period of 23 months, 10/72 patients had a subsequent stroke; 5 of these had low distal flow. Infarct patterns were adjudicated to be consistent with hemodynamic (n = 2), embolic (n = 4), and junctional plaque/perforator (n = 4) infarcts. Hemodynamic infarcts were seen in 40% (2/5) low-flow patients, in comparison to 0% (0/5) normal-flow patients. CONCLUSION: In contrast to normal-flow patients, those with low distal flow seem to be uniquely susceptible to hemodynamic infarctions, although other patterns of infarction can also be seen in these hemodynamically impaired patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".