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Record W4399690007 · doi:10.2337/db24-836-p

836-P: Adherence to App-Based Dose Guidance for Once-Weekly Insulin Icodec in Insulin-Naive Type 2 Diabetes—Post Hoc Analysis of ONWARDS 5

2024· article· en· W4399690007 on OpenAlexaff
Harpreet S. Bajaj, Anders Meller Donatsky, SUSANNE ENGBERG, Johannes H. J. Martiny, ANDRE G. VIANNA, ILDIKO LINGVAY

Bibliographic record

VenueDiabetes · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsBrampton Civic Hospital
Fundersnot available
KeywordsMedicinePost-hoc analysisDosingPost hocInsulinDiabetes mellitusInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Introduction & Objective: In ONWARDS 5, a 52-week, phase 3a trial, participants in the insulin icodec (icodec) group used a dosing guide app to assist titration. This post hoc analysis assessed adherence to app-based dose guidance and its association with prebreakfast self-measured blood glucose (SMBG). Methods: The app provided weekly algorithm-driven icodec dose guidance based on prebreakfast SMBG. Regardless, investigators could suggest manual dose changes. Estimated mean prebreakfast SMBG over time was compared across app-based guidance adherence subgroups (Figure). Results: On average 5.0% of administered icodec doses differed from app guidance (dose change by participant). Of 541 participants assessed, 56.7% had ≥1 manual dose change during the trial; most changes (82%) were increases to the dose recommended by the app. Estimated mean prebreakfast SMBG improved from baseline irrespective of adherence to app guidance. Medium/high adherence subgroups had statistically significantly lower mean prebreakfast SMBG (p<0.0001) with earlier achievement of target level (80-130 mg/dL) than the low adherence subgroup (Figure). Conclusion: Most administered icodec doses were adherent with app guidance; medium/high vs low adherence to app guidance was associated with improved estimated SMBG. The findings support the use of dose guidance apps to assist real-world insulin titration. Disclosure H.S. Bajaj: Research Support; Abbott, Amgen Inc., Anji Pharmaceuticals, Boehringer-Ingelheim, Eli Lilly and Company, Novartis Pharmaceuticals Corporation, Novo Nordisk, Pfizer Inc. A.M. Donatsky: Employee; Novo Nordisk A/S. S. Engberg: Employee; Novo Nordisk A/S. Stock/Shareholder; Novo Nordisk A/S. J.H.J. Martiny: Employee; Novo Nordisk A/S. Stock/Shareholder; Novo Nordisk A/S. A.G. Vianna: Board Member; Abbott, Lilly Diabetes, Novo Nordisk, Medtronic. Speaker's Bureau; AstraZeneca, Novo Nordisk. Research Support; Novo Nordisk, Lilly Diabetes. Speaker's Bureau; Lilly Diabetes. Research Support; Servier Laboratories. I. Lingvay: Consultant; Altimmune, Astra Zeneca, Bayer, Biomea, Boehringer-Ingelheim, Carmot, Cytoki Pharma, Eli Lilly, Intercept, Janssen/J&J, Mannkind, Mediflix, Merck, Metsera, Novo Nordisk, Pharmaventures, Pfizer, Sanofi. Research Support; NovoNordisk, Sanofi, Mylan, Boehringer-Ingelheim. Consultant; TERNS Pharma, The Comm Group, Valeritas, WebMD, and Zealand Pharma. Funding 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.047
GPT teacher head0.414
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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