Long-Term Clinical Outcomes of Oral Antidiabetic Drugs as Fixed-Dose Combinations: A Nationwide Retrospective Cohort Study
Bibliographic record
Abstract
Abstract Objectives: Patients with type 2 diabetes often require more than one medication for glycemic control. Although single pill fixed-dose combinations (FDC) might improve adherence, its impact on clinical outcomes remains unclear. We compared treatment patterns and clinical outcomes of FDC and two-pill combination (TPC) therapies using real-world data.Methods: We conducted a nationwide retrospective cohort study using South Korea’s healthcare database (2002–2015). We identified two cohorts of incident patients with type 2 diabetes who initiated FDC or TPC therapy within 4 months of their first prescription for metformin or sulfonylurea. We examined persistence and adherence patterns and the clinical outcome of a composite endpoint of death or hospitalization for acute myocardial infarction, heart failure, or stroke and compared the differences in treatment patterns and clinical outcomes using Cox models.Results: Of 5,143 and 10,974 patients who initiated FDC and TPC therapy, respectively, we identified 5,143 patient pairs after propensity score matching. The FDC group exhibited greater median time to treatment discontinuation (163 versus 146 days), and proportion of days covered (PDC) at 12 months (mean 0.60 versus 0.57, P < 0.0001) and at 24 months (0.53 versus 0.51, P=0.014) than the TPC group. The FDC group, compared with the TPC group, had reduced risks of the composite clinical outcome (hazard ratio 0.74, 95% confidence intervals 0.57–0.95) and hospitalization for stroke (0.68, 0.49–0.94).Conclusion: FDC therapy may provide favorable cardiovascular benefits, especially reducing the risk of hospitalization for stroke, and have better medication adherence among patients with type 2 diabetes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".