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Record W4281665492 · doi:10.2337/db22-290-or

290-OR: A Randomized Controlled Trial to Alleviate Carbohydrate Counting in Type 1 Diabetes with Automated Fiasp and Pramlintide Closed-Loop Delivery

2022· article· en· W4281665492 on OpenAlexaboutno aff
ELISA COHEN, MICHAEL TSOUKAS, JULIA E. VON OETTINGEN, Jean‐François Yale, Natasha Garfield, MICHAEL VALLIS, NIKITA GOUCHIE-PROVENCHER, ADNAN JAFAR, MILAD GHANBARI, EMILIE PALISAITIS, JOANNA RUTKOWSKI, Laurent Legault, AHMAD HAIDAR

Bibliographic record

VenueDiabetes · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePlaceboType 1 diabetesCrossover studyRandomized controlled trialDiabetes mellitusInternal medicineAnesthesiaEndocrinologyPathology

Abstract

fetched live from OpenAlex

We conducted a non-inferiority randomized crossover trial to alleviate carbohydrate counting (CC) in people with diabetes using automated dual-pump delivery of faster aspart (Fiasp) and pramlintide. Adults (N=15, 9 F, 39 ± 14 years, A1c 7.2 ± 0.9%) and adolescents (N=15, 8 F, 16 ± 1 years, A1c 8.4 ± 0.9%) used (i) Fiasp and placebo with CC, (ii) Fiasp and pramlintide with meal announcement (MA) , and (iii) Fiasp and placebo with MA for 2 weeks. Fiasp and pramlintide were delivered at a fixed 1 U:µg ratio to mimic a co-formulation. MA arms delivered fixed, user-specific priming meal boluses, independent of carbohydrate content. Prior to the first arm, participants had a 1-week run-in with automated Fiasp (single pump) delivery and CC, with mean time in range (70-180 mg/dL) of 71% in adults and 64% in adolescents. In adults, mean time in range was 65% on Fiasp and placebo with CC, 71% on Fiasp and pramlintide with MA, and 64% on Fiasp and placebo with MA; non-inferiority with a pre-defined 6.25% margin was achieved in both MA arms with pramlintide and placebo (difference -6 [95% CI -12.6, 0.5]; 1 [-3.0, 4.6]) . In adolescents, mean time in range was 51%, 55%, and 46% in the three respective arms; non-inferiority was only achieved on Fiasp and pramlintide with MA (-4 [-9.0, 1.7]) . We conclude that automated Fiasp and pramlintide delivery may alleviate CC without degrading glucose control. Disclosure E.Cohen: None. E.Palisaitis: Other Relationship; Eli Lilly and Company. J.Rutkowski: None. L.Legault: Advisory Panel; Abbott Diabetes, Insulet Corporation, Novo Nordisk A/S, Other Relationship; Eli Lilly and Company, Research Support; AstraZeneca, Merck & Co., Inc. A.Haidar: Consultant; Eli Lilly and Company, Research Support; ADOCIA, Dexcom, Inc., Eli Lilly and Company, Tandem Diabetes Care, Inc. M.Tsoukas: Speaker's Bureau; AstraZeneca, Bausch Health, Canada, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Johnson & Johnson, Novo Nordisk Canada Inc. J.E.Von oettingen: None. J.Yale: Advisory Panel; Bayer AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Novo Nordisk Canada Inc., Sanofi, Research Support; Bayer AG, Speaker's Bureau; Abbott Diabetes, AstraZeneca, Bayer AG, Dexcom, Inc., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Canada Inc., Sanofi. N.Garfield: None. M.Vallis: Advisory Panel; Bausch Health, Canada, Novo Nordisk Canada Inc., Consultant; Abbott Diabetes, LifeScan, Speaker's Bureau; AbbVie Inc., Bausch Health, Canada, LifeScan, Novo Nordisk, Novo Nordisk A/S. N.Gouchie-provencher: None. A.Jafar: None. M.Ghanbari: None. Funding Juvenile Diabetes Research Foundation International (2-SRA-2018-654-M-B) , Canada Research Chairs

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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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 designRandomized 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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