Effect of pioglitazone and its combination with statins in coronary artery disease patients with hyperinsulinemia
Bibliographic record
Abstract
The objective of the study was to demonstrate the effect of pioglitazone and pioglitazone in combination with statin on East Indian patients with hyperinsulinemia and hyperlipidemia. It was a randomized, placebo-controlled, double-blind study with a parallel-group design comprising 83 patients. Patients of either sex with cardiac complications, including hyperlipidemia and (or) diabetes mellitus with or without hyperinsulinemia, were enrolled. Patients over 70 years of age, with renal or hepatic failure, or with severe diabetes mellitus (total glucose >400 mg/dL) were excluded from the study. Enrolled patients were randomly assigned to 4 groups that received placebo, pioglitazone, atorvastatin, or both. Blood samples were collected before and after treatment for analysis of serum glucose, insulin, lipid profile, apolipoprotein (apo) A1, apo B, and fibrinogen. Data were compared with that of patients with normal insulin or hyperinsulinemia. The patients with hyperinsulinemia receiving only pioglitazone showed a significant decrease in insulin levels compared with those with normal insulin levels. These patients also showed a significant increase in HDL levels. However, no significant change was observed in patients treated with both atorvastatin and pioglitazone. Pioglitazone was also found to increase significantly the apo A1 levels in patients with hyperinsulinemia, but there was no significant increase in patients given both atorvastatin and pioglitazone. Our data suggests that pioglitazone should be given preferably to the patients with hyperinsulinemia and statin should not be coadministered.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".