MétaCan
Menu
Back to cohort
Record W2902817646 · doi:10.1007/s12265-018-9846-5

Sex and Gender Differences in Ischemic Heart Disease: Endocrine Vascular Disease Approach (EVA) Study Design

2018· article· en· W2902817646 on OpenAlexaff
Valeria Raparelli, Marco Proietti, Andrea Lenzi, Stefania Basili

Bibliographic record

VenueJournal of Cardiovascular Translational Research · 2018
Typearticle
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsMcGill University Health Centre
FundersUniversità degli Studi di FerraraSapienza Università di RomaMinistero dell’Istruzione, dell’Università e della Ricerca
KeywordsDiseaseMedicineInternal medicineEndocrine systemCardiologyHormone

Abstract

fetched live from OpenAlex

Improvements in ischemic heart disease (IHD) management have been unbalanced between sexes, with coronary microvascular dysfunction considered the likely underlying reason. The Endocrine Vascular disease Approach (EVA) is an observational study (Clinicaltrial.gov NCT02737982) aiming to assess sex and gender interactions between coronary circulation, sexual hormones, and platelet function. Consecutive patients with IHD undergoing coronary angiography will be recruited: (1) to assess sex and gender differences in angiographic reperfusion indexes; (2) to evaluate the effects of estrogen/androgen on sex-related differences in myocardial ischemia; (3) to investigate the platelet biology differences between men and women with IHD; (4) to verify sex- and gender-driven interplay between response to percutaneous coronary intervention, platelets, sex hormones, and myocardial damage at baseline and its impact on 12-month outcomes. The integration of sex and gender in this translational project on IHD will contribute to the identification of new targets for further innovative clinical interventions.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0140.002

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 head0.260
GPT teacher head0.415
Teacher spread0.156 · 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
GenreMethods

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

Citations21
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cardiovascular Translational ResearchSame topicSex and Gender in HealthcareFrench-language works237,207