Abstract WP218: Obesity, Burden of Cerebral Small Vessel Disease and Outcomes After Lacunar Stroke:The Secondary Prevention of Small Subcortical Strokes(SPS3) Trial
Bibliographic record
Abstract
Background: Obesity is a recognized risk factor for stroke. But, it is uncertain whether obesity is related to burden of cerebral small vessel disease (CSVD). We explored burden of CSVD, stroke recurrence and mortality in obese and non-obese patients with lacunar infarcts. Method: Data was from the SPS3 (Secondary Prevention of Small Subcortical Strokes) trial. Participants were according to BMI categories: normal /underweight (<24.9), overweight (25.0-29.9), and obese (>30.0). Baseline MRI findings were explored with multivariable logistic regression and outcomes using multivariable Cox proportional hazards. Results: Compared with participants who were normal/underweight (n=691, 23%) overweight (n=1256, 42%) and obese (n=1072, 36%) participants tended to be younger, male and with a higher prevalence of vascular risk factors. Compared to obese participants, normal/underweight had a higher burden of SVD on MRI: odds were 1.40 ( 95% CI 1.15 - 1.69) for severe burden of white matter disease, 1.23 (1.01 - 1.52) for 1+ old lacunes, and, in those 1278 participants with baseline T2*-weighted gradient echo MRI, 1.64 (1.35-2.00) times as likely to have 1+ microbleeds. During a mean follow-up of 3.3 years, overweight and obese subjects also had a lower risk of stroke recurrence (HR 0.74, 95% CI 0.5-1.0 and HR 0.71, 95% CI 0.5-1.0, respectively) and death (HR 0.71 95% CI 0.5-1.0 and HR 0.61, 95% CI 0.4-0.9, respectively). Conclusion: In this cohort, unexpectedly, being overweight or obese was associated with less CSVD, stroke recurrence and mortality. Further research is required to elucidate the mechanisms for this relationship.
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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) | 0.002 | 0.002 |
| 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) | 0.008 | 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".