Abstract 2781: Prevalence and Characterization of Cerebral Microbleeds in Lacunar Stroke Patients: Observations from the Secondary Prevention Small Subcortical Strokes (SPS3) Trial
Bibliographic record
Abstract
Background: The prevalence of cerebral microbleeds (CMBs) has been reported to be high in patients with cerebrovascular disease and particularly in those with cerebral small vessel disease; i.e. lacunar strokes. However, methodological limitations of previous studies imposed by small sample sizes and the absence of a clear classification of stroke subtypes hampers the conclusions regarding prevalence and characterization of CMBs in lacunar stroke patients. Methods: SPS3 is a multi-center international randomized trial including a well-defined cohort of MRI proven lacunar stroke patients. We investigated patients within this population who had an axial T2*- GRE sequence available as part of their baseline clinical MRI. The Brain Observer MicroBleed Scale was used to rate the presence and topography of CMBs. CMB severity was graded as either absent, 1-2 CMBs or >2 CMBs. We compared baseline characteristics and vascular risk factors between patients with CMBs and those without and between the defined CMB severity grades. Results: Of the 651 MRIs analyzed, 237 (36%) had CMBs. Patients with CMBs were more often male (73% vs 62%, p<0.01) and Hispanic (49% vs 39%, p=0.02), were more likely to have hypertension (95% vs 90%, p=0.01) and prior lacunar stroke (13% vs 7%, p=0.01), and had lower baseline mean Cognitive Abilities Screening Instrument (CASI) scores (80 vs 86, p<0.01). Male gender [OR 1.85 (95% CI, 1.26 to 2.70)], prior lacunar stroke [OR 2.19 (95% CI, 1.19 to 4.03)] and lower baseline CASI scores [OR 0.97 (95% CI, 0.95 to 0.98)] remained significant following multivariate analysis. A significant association was found between increasing CMB severity and decreasing CASI scores (p<0.01), an increased presence of multiple lacunar infarcts (p<0.0001) and moderate-severe white matter hyperintensities on MRI (p<0.0001). Conclusions: Preliminary results of this large and well-defined cohort of lacunar stroke suggest a high prevalence of CMBs. A significant association was observed between CMBs and gender and a history of lacunar stroke. CMB severity was associated with multiple infarcts and severity of white matter disease on MRI, as well as lower cognitive function. Ongoing data from SPS3 will likely elucidate whether prognosis and response to therapy differ in patients with CMBs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".