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Record W3108731130 · doi:10.1002/ejhf.1525

November 2020 at a Glance: Focus on Comorbidities and Medical Treatment

2020· article· en· W3108731130 on OpenAlexaff
Daniela Tomasoni, Marianna Adamo, Marco Metra

Bibliographic record

VenueEuropean Journal of Heart Failure · 2020
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsMedicineHeart failureInternal medicineEjection fractionCardiologyHazard ratioValsartanDiabetes mellitusEnalaprilHyperkalemiaConcomitantEndocrinologyAngiotensin-converting enzymeConfidence intervalBlood pressure

Abstract

fetched live from OpenAlex

Comorbidities DiabetesThe role of glycaemic control in patients with heart failure (HF) and concomitant diabetes is controversial.Insulin treatment is associated with poorer outcomes in these patients.1,2 McAlister et al. 3 investigated the relationship between glycaemic control, i.e. glycated haemoglobin (HbA 1c ), and cardiovascular (CV) outcomes in patients with type 2 diabetes mellitus and atherosclerosis from the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS).Results showed a U-shaped association between baseline HbA 1c and CV outcome, with the lowest risk when HbA 1c was around 7%. 3

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.376
Threshold uncertainty score0.890

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.3760.232

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 head0.022
GPT teacher head0.259
Teacher spread0.237 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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
Published2020
Admission routes1
Has abstractyes

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