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Record W4281731350 · doi:10.2337/db22-260-or

260-OR: iGlarLixi vs. Basal plus Rapid-Acting Insulin in Adults with Type 2 Diabetes Advancing from Basal Insulin Therapy: The SoliSimplify Real-World Study

2022· article· en· W4281731350 on OpenAlexaboutno aff
RORY J. MCCRIMMON, ALICE Y. CHENG, GAGIK R. GALSTYAN, Khier Djaballah, XUAN LI, Mathieu Coudert, JUAN PABLO FRIAS

Bibliographic record

VenueDiabetes · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineType 2 diabetesHypoglycemiaRegimenBasal (medicine)Real world evidenceInsulinDiabetes mellitusInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Introduction: Indirect evidence suggests iGlarLixi is as efficacious as basal insulin (BI) + rapid acting insulin (RAI) for management of type 2 diabetes (T2D) . However, there are no direct comparisons of iGlarLixi (once-daily [QD]) vs. a BI+RAI regimen (multiple daily injections [MDI]) . SoliSimplify compared these treatments using real-world data from a US database. Methods: Electronic medical records were analyzed retrospectively using propensity score matching (PSM) to compare therapy advancement with iGlarLixi or BI+RAI in adults ≥18 years with T2D on BI and ≥1 HbA1c available value at baseline and 6-month follow-up. The primary objective was non-inferiority of iGlarLixi to BI+RAI in HbA1c change from baseline to 6 months (margin 0.3 %) . Results: PSM generated cohorts with balanced baseline characteristics (N=814 in each group; Table) . HbA1c reduction from baseline to 6 months with iGlarLixi was non-inferior to BI+RAI (p<0.025) . Weight gain was significantly lower with iGlarLixi than with BI+RAI (p<0.for the difference) . At 6 months, achievement of HbA1c <7 % without hypoglycemia and weight gain was similar between groups. Hypoglycemia was low in both groups, likely due to underreporting. Conclusions: In this real-world study, QD iGlarLixi was as effective as MDI BI+RAI in HbA1c reduction and had a favorable body weight benefit. Disclosure R.J.Mccrimmon: Advisory Panel; Novo Nordisk, Sanofi, Research Support; Diabetes UK, European Union, MedImmune. A.Y.Cheng: Advisory Panel; Abbott, AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Dexcom, Inc., Eli Lilly and Company, HLS Theraoeutics, Insulet Corporation, Janssen Pharmaceuticals, Inc., Medtronic, Novo Nordisk, Sanofi, Board Member; Type 1 Diabetes Think Tank Network, Other Relationship; Diabetes Canada, Speaker's Bureau; Bausch Health, Canada, Merck & Co., Inc. G.R.Galstyan: n/a. K.Djaballah: Employee; Sanofi. X.Li: Employee; Eisai Co., Ltd., Sanofi. M.Coudert: Employee; Sanofi. J.Frias: Advisory Panel; Altimmune, Becton, Dickinson and Company, Eli Lilly and Company, Gilead Sciences, Inc., Intercept Pharmaceuticals, Inc., Merck & Co., Inc., Sanofi, Consultant; 89bio, Inc., Akero Therapeutics, Inc., Altimmune, Becton, Dickinson and Company, Carmot Therapeutics, Inc., Eli Lilly and Company, Novo Nordisk, Pfizer Inc., Sanofi, Research Support; Afimmune Limited, Akero Therapeutics, Inc., AstraZeneca, Boehringer Ingelheim International GmbH, Bristol-Myers Squibb Company, Carmot Therapeutics, Inc., Eli Lilly and Company, Intercept Pharmaceuticals, Inc., Ionis Pharmaceuticals, Janssen Pharmaceuticals, Inc., Madrigal Pharmaceuticals, Inc., Merck & Co., Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk, Pfizer Inc., Poxel SA, Sanofi, Speaker's Bureau; Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk, Sanofi. Funding Sanofi

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)0.004
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.024
GPT teacher head0.292
Teacher spread0.269 · 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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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