Final 2 year results of the vascular imaging of acute stroke for identifying predictors of clinical outcome and recurrent ischemic eveNts (VISION) study
Bibliographic record
Abstract
UNLABELLED: Among patients with ischemic stroke, little attention has been paid to differentiation between stroke progression and recurrence. We assessed the role of MR imaging in predicting stroke progression, recurrent stroke, and death within 2 years of symptom onset. METHODS: Ischemic stroke or TIA patients were prospectively enrolled. They were examined within 12 hours and had a stroke MR completed within 24 hours of symptom onset. Patients were closely followed neurologically and examined if there was any deterioration in neurological status. Relationships between baseline clinical and imaging factors and outcomes were assessed. We also examined whether baseline stroke/TIA severity (NIHSS 0-5 versus NIHSS > 5) modified these relationships. RESULTS: A total of 334 patients were enrolled. The overall rates of progression, 2-year recurrence, and 2-year death were 8.7%, 8.0%, and 6.6%, respectively. Event rates were similar among patients with mild compared to more severe strokes: 8.3% versus 9.5% (p = 0.73) for progression, and 7.3% versus 9.9% (p = 0.59) for recurrence. The effect of baseline glucose > 8 mmol/l was consistent in predicting stroke progression, recurrent stroke and death, regardless of baseline stroke severity. In multivariable analyses, DWI lesion and intracranial occlusion predicted stroke progression only in the minor stroke/TIA group; symptomatic Internal Carotid Artery (ICA) stenosis predicted stroke recurrence only in the minor stroke/TIA group. CONCLUSIONS: In a prospective study with early assessment and imaging we have found that stroke progression is different than stroke recurrence. Different imaging factors predict stroke progression versus stroke recurrence. Baseline hyperglycemia, a potentially modifiable factor, consistently predicted all three outcomes (stroke progression, recurrent stroke or death) regardless of baseline stroke severity.
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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 | 0.003 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".