Once-Weekly Insulin Icodec With Dosing Guide App Versus Once-Daily Basal Insulin Analogues in Insulin-Naive Type 2 Diabetes (ONWARDS 5)
Bibliographic record
Abstract
BACKGROUND: Inadequate dose titration and poor adherence to basal insulin can lead to suboptimal glycemic control in persons with type 2 diabetes (T2D). Once-weekly insulin icodec (icodec) is a basal insulin analogue that is in development and is aimed at reducing treatment burden. OBJECTIVE: To compare the effectiveness and safety of icodec titrated with a dosing guide app (icodec with app) versus once-daily basal insulin analogues (OD analogues) dosed per standard practice. DESIGN: 52-week, randomized, open-label, parallel-group, phase 3a trial with real-world elements. (ClinicalTrials.gov: NCT04760626). SETTING: 176 sites in 7 countries. PARTICIPANTS: 1085 insulin-naive adults with T2D. INTERVENTION: Icodec with app or OD analogue (insulin degludec, insulin glargine U100, or insulin glargine U300). MEASUREMENTS: ) level from baseline to week 52. Secondary outcomes included patient-reported outcomes (Treatment Related Impact Measure for Diabetes [TRIM-D] compliance domain score and change in Diabetes Treatment Satisfaction Questionnaire [DTSQ] total treatment satisfaction score). RESULTS: = 0.009) confirmed in prespecified hierarchical testing (estimated treatment difference [ETD], -0.38 percentage points [95% CI, -0.66 to -0.09 percentage points]). At week 52, patient-reported outcomes were more favorable with icodec with app than with OD analogues (ETDs, 3.04 [CI, 1.28 to 4.81] for TRIM-D and 0.78 [CI, 0.10 to 1.47] for DTSQ). Rates of clinically significant or severe hypoglycemia were low and similar with both treatments. LIMITATION: Inability to differentiate the effects of icodec and the dosing guide app. CONCLUSION: reduction and improved treatment satisfaction and compliance with similarly low hypoglycemia rates. PRIMARY FUNDING SOURCE: Novo Nordisk A/S.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".