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Record W3035359689 · doi:10.1111/jdi.13321

Sodium–glucose cotransporter 2 inhibitors compared with other glucose‐lowering drugs in Japan: Subanalyses of the CVD‐REAL 2 Study

2020· article· en· W3035359689 on OpenAlexaff
Shun Kohsaka, M. Takeda, Johan Bodegård, Marcus Thuresson, Mikhail Kosiborod, Toshitaka Yajima, Eric Wittbrodt, Peter Fenici

Bibliographic record

VenueJournal of Diabetes Investigation · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsAstraZeneca (Canada)
FundersAstraZeneca
KeywordsMedicineHazard ratioInternal medicineHeart failureMetforminDiabetes mellitusConfidence intervalDipeptidyl peptidase-4Stroke (engine)Relative riskType 2 diabetesGastroenterologyEndocrinology

Abstract

fetched live from OpenAlex

There are limited data on cardiovascular efficacy and safety of type 2 diabetes therapies in Japan, where treatments are characterized by lower metformin use and higher dipeptidyl peptidase-4 inhibitor (DPP4i) use versus other countries. We investigated the cardiovascular outcomes in Japanese patients with type 2 diabetes initiating sodium-glucose cotransporter 2 inhibitors (SGLT2i) matched 1:1 to patients initiating other glucose-lowering drugs (33,890 patients/group) or DPP4i (9,876 patients/group). SGLT2i initiation was associated with lower risks (hazard ratio of in-hospital death [death] 0.56, 95% confidence interval [CI] 0.47-0.67; hospitalization for heart failure 0.75, 95% CI 0.64-0.89; composite of hospitalization for heart failure or death 0.65, 95% CI 0.58-0.74 and stroke 0.66, 95% CI 0.52-0.84 versus other glucose-lowering drugs and lower risks of death 0.52, 95% CI 0.36-0.73) and composite of hospitalization for heart failure or death (0.65, 95% CI 0.51-0.83) versus DPP4i. In conclusion, SGLT2i initiators had lower risks of cardiovascular events versus other glucose-lowering drug initiators and, uniquely, versus DPP4i initiators in Japanese real-world practice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.136
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.244
Teacher spread0.223 · 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 teacher head, 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

Citations12
Published2020
Admission routes1
Has abstractyes

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