Abstract WP166: Prediction of Recurrent Stroke in Lacunar Stroke Patients: Performance of the Small Vessel Disease (SVD) Score in the SPS3 Trial
Bibliographic record
Abstract
Introduction: The previously published cerebral SVD Score is a four-point rating scale based on the four cardinal MRI markers of SVD: moderate-severe white matter hyperintensities, , lacune(s), moderate-severe enlarged perivascular spaces (PVS), and microbleed(s). We evaluated the score’s ability to predict recurrent stroke in the Secondary Prevention of Small Subcortical Strokes (SPS3) Trial. Methods: One point each was awarded for these 4 MRI findings: max periventricular Fazekas score = 3 and/or max subcortical Fazekas score > 2; >1 cerebral microbleed 2 (i.e. > 11 PVS unilaterally); > 1 old lacune (3-15 mm) on FLAIR/T1. Annualized rates of recurrent stroke were computed assuming a Poisson model, and c-statistics were calculated for the SVD score model and for two other previously published SPS3 derived models. Results: Of 3020 participants, 1137 had complete data available for SVD scoring. Prevalence of SVD scores 0, 1, 2, 3, and 4 were 19% (n=219), 29% (n=325), 24% (n=277), 18% (n=200), and 10% (n=116). PVS (55%) were most common, followed by moderate-severe white matter hyperintensities (45), lacunes (41), and microbleeds (30). Recurrent stroke rates did not strictly increase with increasing SVD score, i.e. rates were 2.4%/pt-yr (95% CI 1.5, 3.9), 1.4 (0.8, 2.3), 2.0 (1.3, 3.2), 3.8 (2.5, 5.7), and 3.2 (1.8, 5.6) respectively. When SVD scores of 0-2 vs. 3-4 were grouped and compared with two other models for predicting recurrent stroke in this cohort, the SVD score model did not outperform. (Table) Conclusions: SVD score features were very common in the SPS3 cohort. Higher (3-4) vs. lower (0-2) SVD scores predicted recurrent stroke with similar predictive ability to models including clinical risk factors only +/- fewer MRI features. Further testing of the SVD score is warranted.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".