MétaCan
Menu
Back to cohort
Record W4411294790 · doi:10.2337/db25-1995-lb

1995-LB: Achieving Equitable Glycemic Control with Automated Insulin Delivery (AID)—A Real-World Analysis of MiniMed 780G System Use across Canada

2025· article· en· W4411294790 on OpenAlexaboutno aff
ALICE Y. CHENG, MATIAS CASTRO, Jennifer McVean, Richard A. M. Jonkers, Robert A. Vigersky

Bibliographic record

VenueDiabetes · 2025
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
Fundersnot available
KeywordsGlycemicInsulin deliveryControl (management)MedicineInsulinComputer scienceDiabetes mellitusInternal medicineEndocrinologyArtificial intelligence

Abstract

fetched live from OpenAlex

Introduction and Objective: AID systems have markedly improved diabetes glycemic control globally. Their impact within Canada, where provincial variations in management and suboptimal control have been observed1, requires further investigation. This real-world analysis assessed the effect of MiniMed™ 780G (MM780G) system use on glycemic outcomes in Canada. Methods: Carelink™ data (up to January 16th, 2025) of consenting Canadian MM780G users (N=9065, aged 7-79 years) were aggregated and analyzed. Use of recommended optimal settings (ROS) was assessed at a national level, whilst glycemia and insulin delivery were evaluated nationally and by province. Results: Ontario, Quebec and Alberta had the most MM780G users. The consensus-recommended time in range (TIR, 3.9-10mmol/L) goal of >70% was achieved nationally (72.3%) and across provinces (Figure 1). Nearly 50% of users achieved all recommended CGM metric goals. Although ROS use was associated with an increase in TIR and time within 3.9-7.8mmol/L (TITR) by >5% (77.9% and 53.5%, respectively), only 10% of people used ROS. Conclusion: MiniMed™ 780G system use in Canada achieved consensus-recommended glycemic goals that were similar across the provinces, reducing regional disparities. Greater use of ROS would likely improve these results. Future research should investigate barriers to ROS adoption and target interventions that optimize AID utilization nationwide. Disclosure A.Y. Cheng: Advisory Panel; Abbott. Speaker's Bureau; Abbott. Other Relationship; American Diabetes Association. Speaker's Bureau; Amgen Inc, AstraZeneca. Advisory Panel; Bayer Pharmaceuticals, Inc. Speaker's Bureau; Bayer Pharmaceuticals, Inc. Advisory Panel; Boehringer-Ingelheim. Speaker's Bureau; Boehringer-Ingelheim. Advisory Panel; Dexcom, Inc., Eisai, Eli Lilly and Company. Speaker's Bureau; Eli Lilly and Company, GlaxoSmithKline plc. Advisory Panel; Insulet Corporation. Speaker's Bureau; Insulet Corporation. Advisory Panel; HLS Therapeutics. Speaker's Bureau; HLS Therapeutics, Medtronic. Advisory Panel; Novo Nordisk. Speaker's Bureau; Novo Nordisk, Pfizer Inc. Advisory Panel; Sanofi. Speaker's Bureau; Sanofi. Consultant; Novo Nordisk, Applied Therapeutics, Vertex Pharmaceuticals Incorporated. M. Castro: Employee; Medtronic. J.J. McVean: Employee; Medtronic. R. Jonkers: Employee; Medtronic. R.A. Vigersky: Employee; Medtronic.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.486

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.010
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.275
Teacher spread0.264 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueDiabetesSame topicDiabetes Management and ResearchFrench-language works237,207