Abstract TP184: Did Race Impact Risk Factor Control in the SAMMPRIS Trial?
Bibliographic record
Abstract
Background: WASID showed that Blacks with symptomatic intracranial atherosclerosis are at higher risk of recurrent stroke and have a greater vascular risk factor burden than whites. We sought to determine if aggressive medical management (AMM) in SAMMPRIS resulted in equal control of risk factors in Blacks vs. other races and eliminated the racial disparity in outcome. Methods: Data on all 451 SAMMPRIS patients receiving AMM were used to determine risk factor control in Blacks vs. Others. SBP, LDL, and exercise were recorded throughout the trial, averaged (from baseline until primary endpoint), and dichotomized as in/out of target. Chi-square and t-tests were used to compare risk factor control in Blacks vs. Others. For outcome analyses, data from 227 patients randomized to AMM only were used. Time to event curves for the primary endpoint were compared between Blacks vs. Others using the log-rank test and hazard ratios were calculated with Cox proportional hazards regression. Results: There were no differences between the percentage of Blacks (n=104) vs. Others (n=347) that were in-target for SBP (57.1% vs 62.3%, p=0.36), LDL (40.4% vs 48.5%, p=0.14), or exercise (40.4% vs 43.2%, p=0.61) and no difference in mean SBP (135.2 vs 133.3 mm Hg, p=0.19), LDL (80.7 vs 76.3 mg/dL, p=0.19), or PACE exercise score (3.6 vs 3.8, p=0.27). There was a numerically higher primary endpoint rate among Blacks, but no statistically significant difference in outcome (Figure). However, power to detect a HR of 1.5 for Black race (the HR for the primary endpoint in WASID) was only 20%. Conclusions: Control of SBP and LDL, and exercise, which were highly associated with a good outcome in SAMMPRIS, were not different between Blacks and other races. However, the study lacked sufficient power to detect a difference in outcome between blacks and other races and therefore we cannot conclude that reducing racial disparities in risk factor control will lead to reducing disparities in outcome.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".