Patient‐reported outcome results in patients with type 2 diabetes treated with once‐weekly dulaglutide: data from the <scp>AWARD</scp> phase <scp>III</scp> clinical trial programme
Bibliographic record
Abstract
We evaluated patient-reported outcome (PRO) measures from the Assessment of Weekly AdministRation of LY2189265 (dulaglutide) in Diabetes (AWARD) clinical trial programme for dulaglutide (1.5 mg and 0.75 mg) in patients with type 2 diabetes (T2D). The Impact of Weight on Self-Perception (IW-SP), Impact of Weight on Ability to Perform Physical Activities of Daily Living (APPADL), Impact of Weight on Quality of Life-Lite, EQ-5D, Diabetes Treatment Satisfaction Questionnaire (DTSQ), Diabetes Symptom Checklist-Revised and Adult Low Blood Sugar Survey were administered and analysed for changes from baseline in one or more AWARD studies. Significant within-group changes from baseline to the primary time point were observed for several PRO measures across all studies. Compared with insulin glargine, significantly greater improvements in the IW-SP score were observed with dulaglutide 1.5 mg and with both dulaglutide doses in the APPADL score. Both dulaglutide doses resulted in significantly greater improvement in DTSQ scores (all subscales) compared with exenatide. Dulaglutide 1.5 mg also resulted in significantly greater improvement on the DTSQ hyperglycaemia subscale compared with metformin. Overall, these PRO results suggest that dulaglutide is beneficial in the treatment of T2D.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".