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Record W2754917529 · doi:10.1007/s00125-017-4430-0

Bladder cancer in the EMPA-REG OUTCOME trial

2017· letter· en· W2754917529 on OpenAlexaff
Sven Köhler, Jisoo Lee, Jyothis T. George, Silvio E. Inzucchi, Bernard Zinman

Bibliographic record

VenueDiabetologia · 2017
Typeletter
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalUniversity of Toronto
FundersNovo NordiskIntarcia TherapeuticsSanofiAstraZenecaEli Lilly and Company
KeywordsEMPAEmpagliflozinHuman physiologyMedicineCancerDiabetes mellitusInternal medicineBladder cancerOncologyType 2 diabetesEndocrinologyChemistry

Abstract

fetched live from OpenAlex

SGLT2 Sodium-glucose cotransporter 2To the Editor: We read with interest the recently published article in Diabetologia by Tang et al on the risk of cancer in patients with type 2 diabetes treated with sodium-glucose cotransporter 2 (SGLT2) inhibitors [1].We agree with their conclusion that treatment with SGLT2 inhibitors is not associated with a significantly increased risk of cancer.In their analysis of specific cancer types, the authors suggest that SGLT2 inhibitors may be associated with an increased risk of bladder cancer.With respect to empagliflozin, the authors report that the cardiovascular outcomes trial, EMPA-REG OUTCOME, conducted in individuals with type 2 diabetes and established cardiovascular disease, contributed over 50% of individuals and events to their analysis of bladder cancer.We would like to draw attention to data from this trial

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)0.005
metaresearch head score (Gemma)0.031
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 score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0140.014
Insufficient payload (model declined to judge)0.0060.004

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 head0.078
GPT teacher head0.376
Teacher spread0.298 · 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

Citations31
Published2017
Admission routes1
Has abstractyes

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