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Record W4281625132 · doi:10.2337/db22-95-or

95-OR: Impact of Flash Glucose Monitoring in People with Type 2 Diabetes Inadequately Controlled with Noninsulin Antihyperglycemic Therapy: IMMEDIATE study

2022· article· en· W4281625132 on OpenAlexaboutno aff
RUTH E. BROWN, Ronnie Aronson

Bibliographic record

VenueDiabetes · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGlycemicType 2 diabetesHypoglycemiaDiabetes mellitusInternal medicineRandomized controlled trialInsulinEndocrinology

Abstract

fetched live from OpenAlex

Continuous glucose monitoring (CGM) has been shown to improve glycemic outcomes in people with diabetes using insulin therapies. In this multi-site, randomized trial, we studied adults with T2D inadequately controlled with non-insulin antihyperglycemic therapy to evaluate the impact of flash glucose monitoring (FGM) on glycemic and patient-reported outcomes. Participants received FGM + diabetes self-management education (DSME) or matched DSME alone for 16 weeks. Primary outcome was assessed by a blinded CGM device worn at baseline and at outcome. Among 116 participants enrolled (age 58.4 ± 10.1 years; T2D duration 10.1 ± 6.1 years; HbA1c 8.6 ± 1.1%) , the initial 82 completers (41 FGM + DSME, 41 DSME) showed time in range significantly greater in the FGM + DSME arm (76.1 ± 16.9%) compared to DSME arm (64.3 ± 23.2%) (p<0.01) . Time above range was similarly lower in the FGM + DSME arm (21.5 ± 17.8% vs. 31.3 ± 25.6%, p=0.03) . Hypoglycemia was rare in both arms. Glucose monitoring satisfaction scores improved in the FGM + DSME arm only (0.6 ± 0.5 vs. 0.0 ± 0.5, p<0.01) . Change in HbA1c was also greater in the FGM + DSME arm (-0.9 ± 0.9% vs. -0.5 ± 0.9%, p=0.03) . In this interim analysis, FGM users with T2D using non-insulin therapies had significantly greater time in range, satisfaction with glucose monitoring, and a greater reduction in HbA1c. Disclosure R.E.Brown: None. R.Aronson: Consultant; AstraZeneca, Becton, Dickinson and Company, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Gilead Sciences, Inc., HTL Strefa, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk, Sanofi, Research Support; AstraZeneca, Bausch Health, Canada, Bayer AG, Becton, Dickinson and Company, Boehringer Ingelheim International GmbH, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Janssen Pharmaceuticals, Inc., Kowa Pharmaceuticals America, Inc., Medpace, Novo Nordisk, Sanofi, Tandem Diabetes Care, Inc., Xeris Pharmaceuticals, Inc., Zealand Pharma A/S. Funding Abbott Diabetes Care, Inc.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
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)0.0060.001

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 head0.021
GPT teacher head0.297
Teacher spread0.276 · 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 routes1
Has abstractyes

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