Retrospective Study of IDegLira, a New Fixed-Ratio Combination, in Japanese Patients With Type 2 Diabetes Mellitus: Analysis of Background Factors Affecting Effectiveness After 6 Months of Treatment
Bibliographic record
Abstract
Background: The aim of the study was to provide real-world data on the effectiveness and safety of a new fixed-ratio combination, insulin degludec/liraglutide (IDegLira) injection in Japanese patients with type 2 diabetes mellitus (T2DM). Methods: The primary endpoint was the change in glycated hemoglobin (HbA1c) level 6 months after the introduction of IDegLira. We also examined the rate of achievement of target HbA1c 7% and the individualized HbA1c targets set for each patient. Baseline characteristics associated with the change in HbA1c were also assessed. Seventy-five patients with T2DM were included in the analysis. Results: After the initiation of IDegLira, HbA1c decreased significantly from baseline with a change of -1.81% (baseline 9.61% and at 6 months 7.80%; P < 0.001). At baseline, the achievement rate of 7% HbA1c was 2.67% (n = 2), which increased to 36.0% (n = 27) after 6 months of IDegLira introduction (P < 0.05). The attainment rate of individualized HbA1c targets, which were set considering each patient's characteristics, improved from 2.67% (n = 2) to 49.3% (n = 37) (P < 0.001). Regardless of sex, body mass index, estimated glomerular filtration rate, duration of diabetes, or history of glucagon-like peptide-1 receptor agonist use, IDegLira significantly reduced HbA1c, but a higher C-peptide index was associated with a greater reduction in HbA1c. Conclusion: In this study, initiation of IDegLira in a real-world clinical setting was beneficial in lowering HbA1c in Japanese T2DM patients with inadequate glycemic control with existing therapy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".