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Efficacy and safety of canagliflozin by baseline HbA1c and known duration of type 2 diabetes mellitus

2014· article· en· W2134932941 on OpenAlexaff
John Wilding, Lawrence Blonde, Lawrence A. Leiter, Sonia Cerdas, Cindy S.W. Tong, Jacqueline Yee, Gary Meininger

Bibliographic record

VenueJournal of Diabetes and its Complications · 2014
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
FundersJanssen Research and DevelopmentMitsubishi Tanabe Pharma Corporation
KeywordsMedicineCanagliflozinDiabetes mellitusType 2 Diabetes MellitusInternal medicineDuration (music)Baseline (sea)Type 2 diabetesEndocrinology

Abstract

fetched live from OpenAlex

AIMS: Canagliflozin, a sodium glucose co-transporter 2 inhibitor, has demonstrated glycemic improvements across studies of patients with type 2 diabetes mellitus (T2DM). The impact of canagliflozin on HbA1c lowering was assessed by baseline HbA1c and known duration of T2DM. METHODS: This post hoc analysis pooled data from patients with T2DM enrolled in four 26-week, placebo-controlled, Phase 3 studies of canagliflozin (N=2313). Change in HbA1c from baseline to Week 26 was assessed in the overall population and in subgroups by baseline HbA1c (<8.0%, 8.0%-<9.0%, and ≥9.0%) and known duration of T2DM (<5 years, 5-<10 years, and ≥10 years). RESULTS: Relative to placebo, canagliflozin 100 and 300 mg provided greater HbA1c reductions in the overall population. Progressively larger placebo-subtracted reductions in HbA1c with canagliflozin 100 and 300 mg were seen with increasing baseline HbA1c. HbA1c reductions were similar across subgroups based on known duration of T2DM. Both canagliflozin doses were generally well tolerated across subgroups, with a safety and tolerability profile consistent with that seen in Phase 3 studies. CONCLUSIONS: Canagliflozin provided glycemic improvements in patients with T2DM across a range of baseline HbA1c and known duration of T2DM.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.248
Teacher spread0.236 · 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

Citations45
Published2014
Admission routes1
Has abstractyes

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