The effect of <scp>HbA1c</scp> variability on the efficacy of intensive blood pressure control in patients with type 2 diabetes
Bibliographic record
Abstract
AIMS: The efficacy of intensive blood pressure (BP) control remains controversial, and the variability of HbA1c was a risk factor for macrovascular events in patients with type 2 diabetes. We investigated whether the HbA1c variability modifies the efficacy of intensive BP control. METHODS: Data from the Action to Control Cardiovascular Risk in Diabetes Blood Pressure (ACCORD-BP) trial was utilized. K-means clustering was used to cluster patients into three groups based on the HbA1c variability score and baseline HbA1c values. Cox proportional hazard models and generalized linear models were used to measure the subgroup differences in intensive BP control treatment effects. The primary outcome was a composite of nonfatal myocardial infarction (MI), stroke, or death from cardiovascular causes. RESULTS: In patients with low HbA1c variability rather than medium or high HbA1c variability, intensive BP control reduced the risk of the primary outcome on a relative scale (HR 0.60, 95%CI 0.40-0.90, p interaction was 0.03), non-fatal MI (HR 0.61, 95% CI 0.37-1.00, p interaction was 0.04) and stroke (HR 0.19, 95%CI 0.05-0.64, p interaction was 0.02) or absolute scale. Regardless of the variability group, intensive BP control did not reduce the risk of cardiovascular or all-cause mortality (p interaction >0.05) both on relative and absolute risk scales. CONCLUSION: HbA1c variability had effect on the efficacy of intensive BP control and intensive BP control brought a significant macrovascular benefit in patients with type 2 diabetes and low HbA1c variability.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".