Real-world effectiveness, adherence and persistence among patients with type 2 diabetes mellitus initiating dulaglutide treatment
Bibliographic record
Abstract
OBJECTIVES: To assess glycemic effectiveness, adherence and persistence within 6 months of treatment initiation with dulaglutide, a once weekly GLP-1 receptor agonist, in a US real-world setting. METHODS: This retrospective claims analysis included adults (≥18 years) with T2DM from the HealthCore Integrated Research Database, who had HbA1c laboratory results around initiation and within 6 months after initiation. Glycemic control was assessed by change in HbA1c from pre-initiation to post-initiation. Patients were considered adherent if their proportion of days covered (PDC) was ≥0.80; persistence was measured as days of continuous therapy from initiation to 6 months after initiation with no gaps >45 days between fills. RESULTS: Of the 308 analyzed patients, the majority (n = 188; 61%) were adherent to dulaglutide (mean PDC 0.76; SD 0.26), with 115 patients (37%) discontinuing treatment. Mean persistence was 152 days/5 months. Mean HbA1c decreased from 8.49% (SD 1.70, median 8.20%) at baseline to 7.59% (SD 1.51, median 7.30%) at follow-up, corresponding to a mean HbA1c change of -0.90% (95% confidence interval [CI] -1.08 to -0.73; p < .01; median -0.70%). Patients who were adherent to or persistent with dulaglutide experienced larger reductions (-1.14% and -1.12% respectively), as did those without prior GLP-1 RA use (-1.03%). The proportion of patients with HbA1c <7% increased from 18% to 40%. CONCLUSIONS: Dulaglutide was associated with a significant decrease in HbA1c levels 6 months after treatment initiation. Patients who adhered to or persisted with dulaglutide therapy, or were naïve to GLP-1 RA use, experienced greater decreases in HbA1c levels.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".