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Record W3009213612 · doi:10.1016/j.gheart.2018.09.036

MS09.2 Empagliflozin Reduces Mortality In Patients With Type 2 Diabetes and A History of Left Ventricular Hypertrophy: A Sub-analysis of the EMPA-REG OUTCOME Trial

2018· article· en· W3009213612 on OpenAlexaff
Subodh Verma, C. David Mazer, Deepak L. Bhatt, Satish R. Raj, Andrew T. Yan, Atul Verma, Eleuterio Ferrannini, Gudrun Simons, Bernard Zinman, David Fitchett

Bibliographic record

VenueGlobal Heart · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Syncope and Autonomic Disorders
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalLibin Cardiovascular Institute of AlbertaUniversity of TorontoUniversity of CalgarySt. Michael's Hospital
Fundersnot available
KeywordsMedicineEmpagliflozinSinus tachycardiaInternal medicineTachycardiaCardiologyQuality of life (healthcare)Gold standard (test)Intensive care medicineDiabetes mellitusType 2 diabetes

Abstract

fetched live from OpenAlex

Postural tachycardia syndrome (POTS) and inappropriate sinus tachycardia (IST) are diagnostic possibilities for a patient presenting in sinus tachycardia without any clear primary cause. While neither condition leads to increased mortality, either condition could negatively impact quality of life if not treated. A detailed history and physical exam including orthostatic vital signs are the foundation toward identifying other investigations that may be needed to rule out other causes of sinus tachycardia. There are no gold-standard treatments for POTS or IST. Experts agree that a multidisciplinary approach that considers the individual patients’ presentation is key for identifying potential therapeutic options for management.

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)0.001
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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