HbA1c variability and macrovascular benefits of intensive blood pressure control in patients with type 2 diabetes
Bibliographic record
Abstract
Abstract Background Hypertension is the most common and manageable cardiovascular risk factor in patients with type 2 diabetes. However, the optimal target for blood pressure (BP) control in patients with type 2 diabetes remains a subject of debate. Purpose The aim of this study was to classify levels of haemoglobin A1c (HbA1c) variability using a machine learning algorithm and to investigate the impact of HbA1c variability on the effectiveness of intensive BP control. Methods Data from the Action to Control Cardiovascular Risk in Diabetes Blood Pressure (ACCORD-BP) trial were utilised. K-means clustering was utilised to group patients into three categories with low, medium, and high HbA1c variability levels, based on the HbA1c variability score and baseline HbA1c values. The primary outcome was a composite of non-fatal myocardial infarction (MI), stroke, or death from cardiovascular causes. The secondary outcome was all-cause mortality. Results In patients with low HbA1c variability rather than medium or high HbA1c variability, intensive BP control significantly 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 scales. Regardless of the variability group, intensive BP control did not reduce the risk of cardiovascular death or all-cause mortality (P interaction > 0.05) both on relative and absolute risk scales. Conclusions Patients with type 2 diabetes who have low HbA1c variability may benefit from intensive BP control, particularly in significantly reducing the risk of non-fatal MI and stroke.
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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 | 0.001 |
| 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".