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Record W3176418024 · doi:10.2337/db21-731-p

731-P: CGM-Based Measurements for Once-Weekly Insulin Icodec vs. Once-Daily Insulin Glargine U100 in Insulin-Treated Patients with T2D: A Post-hoc Analysis

2021· article· en· W3176418024 on OpenAlexaff
Harpreet S. Bajaj, Rikke Beck Bang, Amoolya Gowda, Mette Koefoed, Peter Senior, Richard M. Bergenstal

Bibliographic record

VenueDiabetes · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsBrampton Civic Hospital
Fundersnot available
KeywordsMedicineInsulin glargineInsulinInternal medicineEndocrinologyRegular insulinDiabetes mellitusHypoglycemia

Abstract

fetched live from OpenAlex

Insulin icodec* (icodec) is a once-weekly basal insulin in clinical development. In a phase 2 treat-to-target trial (NCT03922750) in patients with T2D switching from daily insulin (N=154), icodec with an initial 100% loading dose (LD; doubling the first dose) showed greater time in range (TIR, 70-180 mg/dL) vs. insulin glargine U100 (IGlar U100) at weeks 15 and 16 (primary endpoint), as measured by double-blinded Dexcom G6® CGM. This post hoc analysis compared TIR, time above range (TAR, >180 mg/dL) and below range (TBR, <70 and <54 mg/dL) for icodec (LD and no loading dose - NLD) vs. IGlar U100 throughout the 16-week period. Over the 16 weeks, overall observed mean TIRs were 71.4% for icodec LD, 60.7% for icodec NLD and 64.3% for IGlar U100. TBR<70 mg/dL and TBR<54 mg/dL remained below the recommended targets (<4% and <1%, respectively) in all groups over the 16 weeks. At weeks 15 and 16, proportion of patients achieving a combination of >70% TIR and <4% TBR<70 mg/dL were 64.2% for icodec LD, 40.0% for icodec NLD and 45.8% for IGlar U100. Icodec LD prevented a mild transient increase in TAR observed during the switch with icodec NLD and appeared to lead to a lower TAR than IGlar U100 over the 16 weeks. Overall, switching to icodec LD appeared to result in higher TIR and lower TAR than IGlar U100 through 16 weeks. TBR remained within the recommended targets. *Proposed INN.View largeDownload slideView largeDownload slide DisclosureH. S. Bajaj: Other Relationship; Self; Eli Lilly and Company, Novo Nordisk, Research Support; Self; Amgen Inc., AstraZeneca, Boehringer Ingelheim International GmbH, Gilead Sciences, Inc., Kowa Pharmaceuticals America, Inc., Merck & Co., Inc., Sanofi, Tricida, Inc. R. Beck bang: Employee; Self; Novo Nordisk A/S. A. Gowda: Employee; Self; Novo Nordisk A/S, Stock/Shareholder; Self; Novo Nordisk A/S. M. M. Koefoed: Employee; Self; Novo Nordisk. P. A. Senior: Other Relationship; Self; Novo Nordisk, Vertex Pharmaceuticals Incorporated, Research Support; Self; Novo Nordisk. R. M. Bergenstal: Advisory Panel; Self; Abbott Diabetes, Eli Lilly and Company, Novo Nordisk, Onduo LLC., Roche , Sanofi, United Healthcare , Consultant; Self; Abbott Diabetes, Ascensia , Dexcom, Inc., Eli Lilly and Company, Hygieia, Johnson & Johnson, Medtronic, Novo Nordisk, Onduo LLC., Roche, Sanofi, United Healthcare, Other Relationship; Self; HealthPartners Institute, Research Support; Self; Abbott Diabetes, Dexcom, Inc., Eli Lilly and Company, Helmsley Charitable Trust, Hygieia, Johnson & Johnson, Medtronic, NIDDK, Novo Nordisk, Onduo LLC., Roche, Sanofi, United Healthcare.

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0130.002

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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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 designNon-randomized trial
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
Published2021
Admission routes1
Has abstractyes

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