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Record W2898326314 · doi:10.1111/dom.13565

Day‐to‐day fasting self‐monitored blood glucose variability is associated with risk of hypoglycaemia in insulin‐treated patients with type 1 and type 2 diabetes: A post hoc analysis of the SWITCH Trials

2018· article· en· W2898326314 on OpenAlexaff
J. Hans DeVries, Timothy S. Bailey, Anuj Bhargava, Gregg Gerety, Janusz Gumprecht, Simon Heller, Wendy Lane, Carol Wysham, Bernard Zinman, Britta Anker Bak, ELISE HACHMANN-NIELSEN, Athena Philis‐Tsimikas

Bibliographic record

VenueDiabetes Obesity and Metabolism · 2018
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesEuropean Foundation for the Study of DiabetesAmerican Diabetes AssociationNovo NordiskAustralian Diabetes SocietyNational Institute for Health and Care Research
KeywordsMedicineInsulin degludecInsulin glargineType 2 diabetesInternal medicinePopulationIntermittent fastingPost-hoc analysisEndocrinologyDiabetes mellitus

Abstract

fetched live from OpenAlex

AIMS: To investigate the association between day-to-day fasting self-monitored blood glucose (SMBG) variability and risk of hypoglycaemia in type 1 (T1D) and type 2 diabetes (T2D), and to compare day-to-day fasting SMBG variability between treatments with insulin degludec (degludec) and insulin glargine 100 units/mL (glargine U100). MATERIALS AND METHODS: Data were retrieved from two double-blind, randomized, treat-to-target, two-period (32 weeks each) crossover trials of degludec vs glargine U100 in T1D (SWITCH 1, n = 501) and T2D (SWITCH 2, n = 720). Available fasting SMBGs were used to determine the standard deviation (SD) of day-to-day fasting SMBG variability for each patient and the treatment combination. The association between day-to-day fasting SMBG variability and overall symptomatic, nocturnal symptomatic and severe hypoglycaemia was analysed for the pooled population using linear regression, with fasting SMBG variability included as a three-level factor defined by population tertiles. Finally, day-to-day fasting SMBG variability was compared between treatments. RESULTS: Linear regression showed that day-to-day fasting SMBG variability was significantly associated with overall symptomatic, nocturnal symptomatic and severe hypoglycaemia risk in T1D and T2D (P < 0.05). Day-to-day fasting SMBG variability was significantly associated (P < 0.01) with all categories of hypoglycaemia risk, with the exception of severe hypoglycaemia in T2D when analysed within tertiles. Degludec was associated with 4% lower day-to-day fasting SMBG variability than glargine U100 in T1D (P = 0.0082) and with 10% lower day-to-day fasting SMBG variability in T2D (P < 0.0001). CONCLUSIONS: Higher day-to-day fasting SMBG variability is associated with an increased risk of overall symptomatic, nocturnal symptomatic and severe hypoglycaemia. Degludec has significantly lower day-to-day fasting SMBG variability vs glargine U100.

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)0.008
metaresearch head score (Gemma)0.007
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 score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.008
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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

Citations22
Published2018
Admission routes1
Has abstractyes

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