The effects of rosiglitazone on inflammatory biomarkers and adipokines in diabetic, hypertensive patients.
Bibliographic record
Abstract
OBJECTIVE: To compare the effects of a 12-week treatment course of a rosiglitazone-based versus a metformin- or glyburide-based strategy on inflammatory biomarkers and adipokine levels in hypertensive, type 2 diabetes patients. METHODS: One hundred three treatment-naive patients or patients on monotherapy with either metformin or glyburide, and a hemoglobin A1C (A1C) ≥7.5%, were randomly assigned to either rosiglitazone add-on (4 mg/day ± titration to 8 mg/day) or a combination of metformin (250 mg twice per day [BID] titrated to 500 BID if A1C ≥7.5% and ≤8.0%; 500 mg BID titrated to 1 g BID if A1C >8.0%) and glyburide (2.5 mg BID titrated to 5 mg BID if A1C ≥7.5% and ≤8.0%; 5 mg BID titrated to 10 mg BID if A1C >8.0%). RESULTS: Rosiglitazone add-on produced significantly greater reductions in high-sensitivity C-reactive protein (2.1 mg/L to 0.9 mg/L) and increases in adiponectin (8.7 mg/mL to 14.8 mg/mL) levels compared with metformin/glyburide (both P<0.005). At close-out, all patients had improved fasting plasma glucose and A1C levels (8.5% to 7.4% and 8.8% to 7.1% for rosiglitazone add-on and metformin-glyburide, respectively [P<0.001 for both arms]) relative to the corresponding baseline values. CONCLUSIONS: The present study demonstrated that in hypertensive, diabetic subjects, a rosiglitazone-based treatment strategy results in favourable changes in inflammatory biomarkers compared with metformin/glyburide.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".