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Record W3035210110 · doi:10.2337/db20-944-p

944-P: HbA1c Change Is Associated with Retinopathy Outcomes during GLP-1RA CVOT Follow-Up

2020· article· en· W3035210110 on OpenAlexaboutno aff
Angelyn Bethel, Rafael Emilio Bello Díaz, Noelia Castellana, Hertzel C. Gerstein, Mark Lakshmanan

Bibliographic record

VenueDiabetes · 2020
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRetinopathyDiabetic retinopathyOdds ratioDiabetes mellitusInternal medicineType 2 diabetesUnivariate analysisOphthalmologyEndocrinologyMultivariate analysis

Abstract

fetched live from OpenAlex

Long-term glucose control reduces retinopathy risk. We examine the association between HbA1c change and retinopathy in the first year or at final follow-up in GLP-1 RA cardiovascular outcomes trials (CVOTs). A random-effects model meta-analysis included 6 CVOTs reporting retinopathy events, using within-trial event definitions. Univariate meta-regression analyses describe the association between HbA1c change and retinopathy events. Meta-analysis showed no significant effect of GLP-1 RA on retinopathy risk (odds ratio [OR] 1.10; 95% CI 0.93, 1.30), with moderate heterogeneity between studies (I2=52.2%; Q-statistic p-value=0.063). HbA1c change and retinopathy were significantly associated at 1-year (slope=1.40, p-value=0.002) and overall (Figure), with 0.1% HbA1c reduction associated with 15% increased OR in the first year, declining to 8.0% increase over longer follow-up. In these studies of varying duration (1.3 to 5.4 years), GLP-1 RA treatment was not significantly associated with increased retinopathy risk. HbA1c reduction rate is correlated with risk for retinopathy in people with diabetes and additional CV risk factors. Clinicians should consider retinopathy status when initiating any therapy that rapidly lowers HbA1c. Disclosure A. Bethel: Employee; Self; Eli Lilly and Company. R. Diaz: None. N. Castellana: None. H.C. Gerstein: Advisory Panel; Self; Abbott, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Consultant; Self; Kowa Pharmaceuticals America, Inc. Research Support; Self; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Other Relationship; Self; Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Sanofi. M. Lakshmanan: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. Stock/Shareholder; Spouse/Partner; Eli Lilly and Company. Funding Eli Lilly and Company

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)0.007
metaresearch head score (Gemma)0.029
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: none
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.015
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0180.001

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 head0.032
GPT teacher head0.253
Teacher spread0.221 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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