High‐density lipoprotein‐cholesterol and not HbA1c was directly related to cardiovascular outcome in PROactive
Bibliographic record
Abstract
AIM: In PROactive, pioglitazone reduced the incidence of death, myocardial infarction and stroke, and significantly improved HbA1c, systolic blood pressure (SBP), triglycerides and high-density lipoprotein (HDL)-cholesterol relative to placebo. As these glycaemic and lipid parameters are major cardiovascular (CV) risk factors, we assessed their separate contribution to the reduced incidence of CV outcomes. METHODS: Patients (n = 5238) with type 2 diabetes and macrovascular disease were randomized to 45 mg pioglitazone or placebo. Relationships among treatment, outcome (time to first event of all-cause mortality, myocardial infarction and stroke) and 10 laboratory measurements and vital signs were investigated using log-linear models. Continuous variable measurements (percent changes from baseline to average of all postbaseline values prior to censoring) were made discrete by categorizing into tertiles. Log-linear models were fitted to multiway tables of discrete data and analysis of deviance used to summarize sources of variation in the data. RESULTS: Although pioglitazone treatment was associated with a decrease in HbA1c and an increase in HDL-cholesterol (HDL-C), only the change from baseline HDL-C predicted the outcome (χ(2) = 28.89, p < 0.0001). No other variables, including HbA1c, triglycerides and systolic blood pressure, showed significant direct associations with outcome. When the analysis was extended to include baseline statin use, this was associated with an improved outcome independently of HDL-C changes. CONCLUSIONS: This post hoc analysis suggests that HDL-C, but probably not HbA1c, is a driver of pioglitazone's favourable influence on CV 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".