MétaCan
Menu
Back to cohort
Record W2606453842 · doi:10.2337/dc17-0237

Use of Canagliflozin in Kidney Transplant Recipients for the Treatment of Type 2 Diabetes: A Case Series

2017· letter· en· W2606453842 on OpenAlexaff
Harindra Rajasekeran, S. Joseph Kim, Carl J. Cardella, Jeffrey Schiff, Mark S. Cattral, David Z.I. Cherney, Sunita Singh

Bibliographic record

VenueDiabetes Care · 2017
Typeletter
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsToronto General HospitalUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsCanagliflozinMedicineDiabetes mellitusType 2 diabetesKidney transplantSeries (stratigraphy)Internal medicineKidney diseaseIntensive care medicineKidneyUrologyKidney transplantationEndocrinology

Abstract

fetched live from OpenAlex

Diabetes is highly prevalent in kidney transplant recipients (KTR). Simultaneous pancreas-kidney transplant recipients (SPKTR) are also at risk for developing type 2 diabetes following transplantation, when insulin secretion may be insufficient to maintain normoglycemia. Transplant-specific risk factors associated with the development of type 2 diabetes include the use of diabetogenic immunosuppressive medications, hypomagnesemia, and posttransplant weight gain (1). In nontransplant populations with type 2 diabetes and established cardiovascular (CV) disease, the use of sodium–glucose cotransporter 2 inhibitors (SGLT2i) can improve glycemic control, promote weight loss, and reduce the risk of CV events (2). Given the increased incidence of posttransplant diabetes and the high CV burden in transplant recipients, the use of SGLT2i in this population is attractive. Of concern, however, is the lack of safety data regarding SGLT2i in transplant recipients. The purpose of this study is to describe our short-term experience of KTR and SPKTR treated with canagliflozin at our institutions. All adult KTR or SPKTR …

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.033
GPT teacher head0.262
Teacher spread0.228 · 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.

Study designNot applicable
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

Citations73
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueDiabetes CareSame topicDiabetes Treatment and ManagementFrench-language works237,207