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Record W4281623698 · doi:10.2337/db22-74-lb

74-LB: Real-World Glycemic Outcomes in Adult Patients with Type 1 Diabetes Using a Real-Time Continuous Glucose Monitor Compared with an Intermittently Scanned Glucose Monitor and Self-Measured Blood Glucose—A Retrospective Observational Study from the Canadian

2022· article· en· W4281623698 on OpenAlexaffabout
RUTH E. BROWN, LISA CHU, Gregory J. Norman, Alexander Abitbol

Bibliographic record

VenueDiabetes · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsSystems, Applications & Products in Data Processing (Canada)
Fundersnot available
KeywordsGlycemicMedicineContinuous glucose monitoringTarget rangeCohortType 1 diabetesDiabetes mellitusInternal medicineType 2 diabetesCohort studyEndocrinology

Abstract

fetched live from OpenAlex

Real-time continuous glucose monitoring (rtCGM) and intermittently scanned CGM (isCGM) have both been shown to improve glycemic outcomes in people with T1D. In this retrospective analysis, we propensity score matched CGM naïve adults with T1D who initiated a rtCGM or an isCGM device. HbA1c and CGM metrics were assessed at 6-12-months. Among the 143 matched patients/cohort (age 43 ± 14 years; T1D duration 22 ± 14 years; HbA1c 8.4 ± 1.0%) , rtCGM users had a significantly greater change in HbA1c (-0.7 ± 0.1%) compared to isCGM users (-0.4 ± 0.1%) (p=0.01) . There was a significantly greater change in HbA1c for rtCGM compared to isCGM when baseline HbA1c was <8.5% (difference -0.4% [-0.6 to -0.2], p<0.001) , and in MDI users (difference -0.3% [-0.5 to 0.0], p=0.04) . Compared to isCGM users, rtCGM users had significantly greater time in range (58.3 ± 16.1% vs. 54.5 ± 17.1%, p=0.03) , lower time below range (2.1 ± 2.7% vs. 6.1 ± 5.0%, p<0.001) and lower glycemic variability (Table) . In this real-world analysis of adults with T1D, rtCGM users had a significantly greater reduction in HbA1c at 6-12 months, significantly greater time in range, lower time below range, and lower glycemic variability, compared to a matched cohort of isCGM users. Disclosure R. E. Brown: None. L. Chu: None. G. J. Norman: Employee; Dexcom, Inc. A. Abitbol: Consultant; Amgen Inc., AstraZeneca, Bayer Inc., Boehringer Ingelheim International GmbH, Dexcom, Inc., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Novo Nordisk, Other Relationship; AstraZeneca, Bayer Inc., Boehringer Ingelheim International GmbH, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk, Novo Nordisk, Sanofi, Senseonics. Funding DexCom Canada, Co.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.405
Threshold uncertainty score0.815

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.267
Teacher spread0.245 · 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
Published2022
Admission routes2
Has abstractyes

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