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Record W3209688304 · doi:10.15420/ecr.2021.22

Women and Diabetes: Preventing Heart Disease in a New Era of Therapies

2021· review· en· W3209688304 on OpenAlexfundno aff
Giuseppe Galati, Pierre Sabouret, Olga Germanova, Deepak L. Bhatt

Bibliographic record

VenueEuropean Cardiology Review · 2021
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsnot available
FundersDaiichi Sankyo EuropeCleveland ClinicIdorsia PharmaceuticalsAmarin CorporationAstraZenecaBristol-Myers SquibbCSL BehringDuke Clinical Research InstituteHLS TherapeuticsAmgenEisaiAmerican Heart Association
KeywordsMedicineType 2 diabetesHeart failureDiseaseIntensive care medicineDiabetes mellitusIncretinAliskirenInternal medicineEndocrinologyRenin–angiotensin systemBlood pressure

Abstract

fetched live from OpenAlex

Despite major advances in cardiovascular research over the past decade, women with type 2 diabetes have a high risk of cardiovascular events. Several factors contribute to the poor prognosis for women, including higher levels of frailty and comorbidities, but their cardiovascular risk is underestimated and there is suboptimal implementation and uptitration of new evidence-based therapies, leading to high morbidity and mortality. Recent studies highlight the need for better management of diabetes in women that can be pursued and achieved in light of recent results from randomised controlled trials demonstrating evidence of the benefits of new therapeutic strategies in improving cardiovascular outcomes and quality of life of women covering the entire cardiovascular continuum. This review critically discusses the multiple benefits for women of new pharmacological treatments, such as glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter type 2 inhibitors (SGLT2i), proprotein convertase subtilisin/kexin type 9 inhibitors, inclisiran, icosapent ethyl and bempedoic acid in preventing cardiovascular events, and treatments, such as angiotensin receptor neprilysin inhibitors, SGLT2i, vericiguat and omecamtiv mecarbil, for preventing heart failure.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
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 head0.040
GPT teacher head0.308
Teacher spread0.268 · 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 designOther design
Domainnot available
GenreReview

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

Citations14
Published2021
Admission routes1
Has abstractyes

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