The Influences of Withdrawal and Daily Dose Reduction of Pioglitazone on Metabolic Parameters in Patients With Type 2 Diabetes: A Retrospective Longitudinal Observational Study
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to understand the influences of withdrawal or dose reduction of pioglitazone in patients with type 2 diabetes. METHODS: We retrospectively picked up patients who had undergone withdrawal or daily dose reduction of pioglitazone after a continuous prescription for 3 months or longer between January 2010 and March 2014. We compared the data before the withdrawal or dose reduction of pioglitazone with the data at 3 or 6 months after those by a chart-based analysis. RESULTS: Among 713 patients taking pioglitazone at least once during the studied period, 20 patients had undergone withdrawal of pioglitazone (group A) and 51 patients had undergone daily dose reduction (group B). The mean pioglitazone dose at baseline was 23 mg in subjects of group A, and 30 mg in group B. The number of subjects who had taken high-dose metformin (≥ 1,000 mg) and dipeptidyl peptidase-4 (DPP-4) inhibitors increased after the withdrawal or dose reduction of pioglitazone in both groups. Although no significant change was observed in plasma glucose and HbA1c levels, body weight significantly decreased at 3 and 6 months after the dose reduction in group B. The same tendency was observed in group A. Serum high-density lipoprotein-cholesterol (HDL-C) levels significantly decreased at 3 and 6 months after the withdrawal in group A. The serum alanine aminotransferase levels significantly increased 3 months after the withdrawal in group A. CONCLUSIONS: Present study demonstrated that the withdrawal of pioglitazone exacerbated serum HDL-C and liver function in patients with type 2 diabetes, although glycemic control could be maintained by using high-dose metformin or DPP-4 inhibitors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".