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Record W4388266064 · doi:10.24875/acm.m23000089

Arritmias en personas transgénero

2023· article· es· W4388266064 on OpenAlexaff
Ana C. Berni, Rachel Wamboldt, Adrián Baranchuk

Bibliographic record

VenueArchivos de cardiología de México · 2023
Typearticle
Languagees
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsKingston Health Sciences CentreQueen's University
Fundersnot available
KeywordsMedicineTransgenderPopulationEndocrinologyGynecologyInternal medicinePsychologyEnvironmental health

Abstract

fetched live from OpenAlex

The need to improve access to health services for the transgender community has become evident, especially concerning cardiovascular risk, which is higher compared to the general population. Surgical procedures and hormone therapies are common in this population to affirm gender identity, but they pose challenges as they are associated with disruptions in lipid metabolism, body fat concentration, and insulin resistance. Additionally, there is an increased risk of adverse cardiovascular events such as venous thromboembolism, stroke, and myocardial infarction. The influence of sex hormones on the electrophysiological properties of the heart has been studied, highlighting gender differences that may predispose the transgender population to cardiac arrhythmias. Exogenous hormone therapy, for both transgender women and men, can affect the QT interval and increase the risk of arrhythmias, including atrial fibrillation. Although the incidence of arrhythmias in the transgender population is not entirely clear, evidence suggests the need for careful cardiovascular monitoring and consideration of risk factors before initiating hormone therapies.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.023
GPT teacher head0.301
Teacher spread0.279 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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