Effect of Switching From an Anti-Diabetic Loose Dose Combination to a Fixed Dose Combination Regimen at Equivalent Dosage for 6 Months on Glycemic Control in Japanese Patients With Type 2 Diabetes: A Pilot Study
Bibliographic record
Abstract
Background: Patients with type 2 diabetes mellitus often take multiple anti-diabetic drugs for a long period. Fixed dose combination (FDC) therapy is expected to improve drug adherence for patients with diabetes. The effect of switching from a loose dose combination (LDC) regimen to an FDC regimen at equivalent dosage on glycemic control has not been evaluated fully. Therefore, we investigated the effect of switching from LDC to FDC at equivalent dosage for 6 months on glycemic control in Japanese patients with type 2 diabetes. Methods: Thirty-eight Japanese patients with type 2 diabetes who were taking anti-diabetic drugs including pioglitazone + metformin, pioglitazone + alogliptin, or pioglitazone + glimepiride were enrolled. These drugs were switched to an FDC of Metact ® , Liobel ® or Sonias ® , respectively, at equivalent dosage. Other anti-diabetic drugs and units of insulin were not changed during the study if possible. HbA1c and body weight were measured 0, 2, 4 and 6 months after switching from an LDC to FDC. We also conducted a questionnaire survey 2 months after the start of the FDC regimen. Results: HbA1c levels at 2, 4, and 6 months were not significantly changed compared with prior to switching from an LDC to FDC regimen. Moreover, 74.2% of patients considered decreasing the number of drugs to be “very good” or “good” . Conclusion: HbA1c levels did not differ between patients receiving LDC and FDC therapy at equivalent dosage in this study. J Clin Med Res. 2017;9(8):719-724 doi: https://doi.org/10.14740/jocmr3067w
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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 | 0.001 | 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) | 0.001 | 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".