Abstract 66: MRI Predictors of Stroke Recurrence in Patients With Recent Lacunar Stroke: The SPS3 Trial
Bibliographic record
Abstract
Background: Brain infarcts (BIs) and white matter hyperintesities (WMHs) have been associated with an increased risk of stroke. However, neither has been investigated in a well characterized cohort of patients with cerebral small vessel disease. Our objective was to identify MRI predictors of recurrent stroke in the SPS3 cohort. Methods: SPS3 was a multicenter trial that enrolled 3020 patients with recent lacunar stroke verified by MRI. Baseline MRIs were centrally read, including presence of index infarct, old subcortical brain infarcts (also called lacunar infarcts) (BIs), and WMHs using the ARWMC score, and independent predictors of recurrent stroke identified. A stratification scheme for stroke risk was developed based on MRI findings and previously identified clinical risk factors (RF) of prior stroke/TIA, black race, male, diabetes. Results: 2980 MRIs were included in these analyses. BIs (HR 1.5, 95% CI 1.2, 1.9) and more severe WMHs (HR 1.4, CI 1.2, 1.6) were independently associated with recurrent stroke, and remained independent after including clinical RF and assigned treatments in the Cox PH model. Anatomical localization of the index stroke was not associated with stroke recurrence. Annualized recurrent stroke rates were 5.2 %/pt-yr (95% CI 4.3, 6.2), 2.7% (95% CI 2.2, 3.4), and 1.7% (95% CI 1.1, 1.7) respectively in high (> 2 of the following: prior stroke/TIA, BIs, severe WMD, or all 3 other clinical RF), moderate (not high risk; prior stroke/TIA or 2 clinical RF), and low (not moderate or high risk; 0-1 clinical RF) risk patients. Conclusions: In this large, well-characterized cohort of recent lacunar strokes, old subcortical infarcts and WMHs on MRI are shown to be independent predictors of stroke recurrence. Stratification schemes may help to identify patients at different risk and select the most appropriate therapeutic intervention for stroke reduction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".