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Record W4206445899 · doi:10.1056/nejmoa1901118

Oral Semaglutide and Cardiovascular Outcomes in Patients with Type 2 Diabetes

2019· article· en· W4206445899 on OpenAlexaff
Mansoor Husain, Andreas L. Birkenfeld, Morten Donsmark, Kathleen Dungan, Freddy G. Eliaschewitz, Denise Reis Franco, Ole Kleist Jeppesen, Ildiko Lingvay, Ofri Mosenzon, Sue D. Pedersen, Cees J. Tack, Mette S. Thomsen, Tina Vilsbøll, Mark Warren, Stephen C. Bain

Bibliographic record

VenueNew England Journal of Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsLMC Diabetes & Endocrinology (Canada)Ted Rogers Centre for Heart Research
FundersNovo NordiskSteno Diabetes Center Copenhagen
KeywordsSemaglutideType 2 diabetesMedicineDiabetes mellitusInternal medicineCardiologyEndocrinology

Abstract

fetched live from OpenAlex

Establishing cardiovascular safety of new therapies for type 2 diabetes is important. Safety data are available for the subcutaneous form of the glucagon-like peptide-1 receptor agonist semaglutide but are needed for oral semaglutide.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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 designRandomized trial
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

Citations1,932
Published2019
Admission routes1
Has abstractyes

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