<scp>Long‐term</scp> clinical outcomes of oral antidiabetic drugs as <scp>fixed‐dose</scp> combinations: A nationwide retrospective cohort study
Bibliographic record
Abstract
AIM: To compare treatment patterns and clinical outcomes of single-pill fixed-dose combination (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 sulphonylurea. 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 5143 and 10 973 patients who initiated FDC and TPC therapy, respectively, we identified 5143 patient pairs after propensity score matching. The FDC group exhibited greater median time to treatment discontinuation (163 vs. 146 days), and proportion of days covered at 12 months (mean 0.60 vs. 0.57, P < .0001) and at 24 months (0.53 vs. 0.51, P = .014) than the TPC group. The FDC group, compared with the TPC group, had reduced risks of the composite clinical outcome (hazard ratio 0.86, 95% confidence intervals 0.77-0.97) and hospitalization for stroke (0.80, 0.67-0.96). CONCLUSION: FDC therapy may provide favourable cardiovascular benefits, especially reducing the risk of hospitalization for stroke, and has better medication adherence among patients with type 2 diabetes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".