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Record W2809518459 · doi:10.2337/db18-10-or

Analysis of Prevalence, Magnitude, and Timing of the Dawn Phenomenon in Type 1 Diabetes—Descriptive Analysis of Two Insulin Pump Trials

2018· article· en· W2809518459 on OpenAlexaboutno aff
Ilia Ostrovski, Leif Erik Lovblom, Daniel Scarr, Alanna Weisman, Andrej Orszag, Émilie D'Aoust, Ahmad Haidar, Rémi Rabasa‐Lhoret, Laurent Legault, Bruce A. Perkins

Bibliographic record

VenueDiabetes · 2018
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGlycemicDiabetes mellitusMagnitude (astronomy)InsulinBasal (medicine)Type 1 diabetesArtificial pancreasInternal medicineEndocrinologyPhysics

Abstract

fetched live from OpenAlex

To better understand the dawn phenomenon in type 1 diabetes, we sought to determine its prevalence, timing and magnitude in studies specifically designed to assess insulin pump basal requirements. Thirty-three participants from two insulin pump studies were analyzed. Twenty were obtained from a methodologically-ideal semi-automated basal analysis trial in which basal rates were determined from repeated fasting tests (the derivation set) and 13 from an artificial pancreas trial in which duration of fasting was variable (the validation set). Prevalence was determined for the total cohort and the individual trials using the standard definition of an increase in insulin exceeding 20% and lasting 90 minutes or more. Among cases, time of onset and percent change in the magnitude of basal delivery was determined. Seventeen of the 33 (52%) participants experienced the dawn phenomenon [11 of 20 (55%) in the derivation set, 6 of 13 (46%) in the validation set]. Time of onset was 3:00 am [IQR; 3:00, 4:15 am] in the derivation set and 3:00 am [3:00, 4:00 am] in the validation set. The magnitude of the dawn phenomenon was a 58.1% [28.8, 110.6%] increase in insulin requirements in the derivation set and 65.5% [45.6%, 87.4%] in the validation set. The dawn phenomenon occurs in approximately half of patients with type 1 diabetes, when present it has predictable timing of onset (generally 3am), and has substantial but highly variable magnitude. These findings imply that optimization of glycemic control requires clinical emphasis on fasted overnight basal insulin assessment. Disclosure I. Ostrovski: None. L. Lovblom: None. D. Scarr: None. A. Weisman: None. A. Orszag: None. E. D'Aoust: None. A. Haidar: Consultant; Self; Eli Lilly and Company. Research Support; Self; AgaMatrix, Medtronic MiniMed, Inc. R. Rabasa-Lhoret: Consultant; Self; Abbott, Amgen Inc.. Other Relationship; Self; Animas Corporation. Consultant; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc.. Research Support; Self; Diabetes Canada, Canadian Institutes of Health Research. Other Relationship; Self; Eli Lilly and Company. Consultant; Self; Janssen Pharmaceuticals, Inc.. Research Support; Self; JDRF. Consultant; Self; Medtronic. Other Relationship; Self; Merck & Co., Inc., Novo Nordisk Inc., Sanofi-Aventis. Advisory Panel; Self; Sanofi US. Research Support; Self; National Institutes of Health, Cystic Fibrosis Canada, Société Francophone du Diabéte. L. Legault: Advisory Panel; Self; Insulet Corporation. Research Support; Self; Merck & Co., Inc., Sanofi. Advisory Panel; Self; Medtronic. Other Relationship; Self; Eli Lilly and Company. B.A. Perkins: Advisory Panel; Self; Boehringer Ingelheim GmbH. Research Support; Self; Boehringer Ingelheim GmbH, Novo Nordisk Inc.. Advisory Panel; Self; Novo Nordisk Inc., Abbott. Speaker's Bureau; Self; Abbott, Janssen Pharmaceuticals, Inc.. Advisory Panel; Self; Insulet Corporation. Speaker's Bureau; Self; Insulet Corporation, Dexcom, 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)0.024
metaresearch head score (Gemma)0.053
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 score0.024
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.053
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.062
GPT teacher head0.346
Teacher spread0.284 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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