Predictors of lowering SBP to assigned targets at 12 months in the Secondary Prevention of Small Subcortical Strokes study
Bibliographic record
Abstract
OBJECTIVE: Lowering blood pressure for secondary stroke prevention remains a challenge. These analyses were conducted to identify factors predicting achievement of SBP targets in the Secondary Prevention of Small Subcortical Strokes (SPS3) study. METHODS: SPS3 is a randomized trial assigning patients with lacunar stroke to two targets of SBP control (130-149 mmHg or <130 mmHg). Logistic regression models were used to identify patient and SPS3 site characteristics predictive of lowering SBP to target at the 12-month study visit. RESULTS: Of those above target at baseline (n = 1041), 69% were within their assigned target at 12 months. In the model with baseline characteristics only, those receiving treatment for hypertension at baseline were 68% less likely to achieve target [odds ratio (OR) = 0.32; 95% confidence interval (CI) = 0.17-0.60], whereas those of Hispanic ethnicity were 1.49 times more likely (95% CI = 1.09-2.03) to achieve SBP target. When clinical site characteristics were added to the model, only treated hypertension at baseline remained significant. In addition, management at a larger site (OR = 1.51; 95% CI = 1.03-2.20), SBP in target at 6 months (OR = 2.39; 95% CI = 1.79-3.19), and medication adherence (OR = 2.73; 95% CI = 1.51-4.95) were positively associated with achieving target SBP. Missed appointments (OR = 0.55; 95% CI = 0.41-0.73) were negatively associated with lowering SBP to target at 12 months. CONCLUSION: These results demonstrate that it is feasible to achieve targets of SBP control in this multiethnic stroke cohort across multiple sites and countries. The results highlight the important variables reflecting clinical site management.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".