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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".