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Record W3128163814 · doi:10.2217/cer-2020-0137

Heterogeneity in renal end points of cardiovascular outcomes trials in Type 2 diabetes

2021· article· en· W3128163814 on OpenAlexaff
Chakrapani Balijepalli, Kevin Yan, Michael J. Zoratti, Meg Franklin, Eric Druyts

Bibliographic record

VenueJournal of Comparative Effectiveness Research · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsMedicineEnd stage renal diseaseRenal functionType 2 diabetesClinical endpointInternal medicineIntensive care medicineClinical trialCreatinineDiabetes mellitusUrologyDiseaseEndocrinology

Abstract

fetched live from OpenAlex

Composite renal end points and end stage renal disease (ESRD) are frequently included as prespecified secondary end points in the cardiovascular outcomes trials (CVOTs) of diabetes medications. We examined the heterogeneity in the definitions of composite renal end point and ESRD in CVOTs. Five criteria (macroalbuminuria, doubling of serum creatinine, estimated glomerular filtration rate [GFR], ESRD and renal death), were considered for the renal composite end point across the trials. Only three of the 12 trials included all five criteria, whereas the other trials included different combinations of four, three and two criteria. ESRD definition also showed considerable heterogeneity across the trials. Heterogeneity exists in the definitions of renal composite and ESRD end points in CVOTs making it challenging to assess comparative efficacy of the active treatments for reimbursement purposes.

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)0.256
metaresearch head score (Gemma)0.399
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.744
Threshold uncertainty score0.918

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2560.399
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.015
Bibliometrics0.0050.005
Science and technology studies0.0010.002
Scholarly communication0.0060.004
Open science0.0020.003
Research integrity0.0030.003
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 head0.206
GPT teacher head0.471
Teacher spread0.266 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainMethods
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

Citations3
Published2021
Admission routes1
Has abstractyes

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