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Record W4294300928 · doi:10.1152/ajpheart.00299.2022

Gender-affirming estrogen therapy route of administration and cardiovascular risk: a systematic review and narrative synthesis

2022· review· en· W4294300928 on OpenAlexafffund
Keila Turino Miranda, Cindy Z. Kalenga, Nathalie Saad, Sandra M. Dumanski, David Collister, Chantal L. Rytz, Diane Lorenzetti, Danica H. Chang, Caitlin McClurg, Darlene Y. Sola, Sofia B. Ahmed

Bibliographic record

VenueAmerican Journal of Physiology-Heart and Circulatory Physiology · 2022
Typereview
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsAlberta Kidney Disease NetworkLibin Cardiovascular Institute of AlbertaUniversity of AlbertaUniversity of Calgary
FundersAlberta Health Services
KeywordsEstrogenTransgenderEstrogen therapyMedicineNarrative reviewRisk stratificationAdverse effectProspective cohort studyInternal medicineIntensive care medicinePsychology

Abstract

fetched live from OpenAlex

This study is the first to summarize the potential effect of nonoral versus oral gender-affirming estrogen therapy use on cardiovascular risk factors in transgender women or nonbinary or gender-diverse individuals. Heterogeneity of studies in reporting gender-affirming estrogen therapy formulation, dose, and duration of exposure limits quantification of the effect of gender-affirming estrogen therapy on all-cause and cardiovascular mortality, adverse cardiovascular events, and cardiovascular risk factors. This systematic review highlights the needs for large prospective cohort studies with appropriate stratification of gender-affirming estrogen therapy by dose, formulation, administration route, and sufficient follow-up and analyses to limit selection bias to optimize the cardiovascular care of transgender, nonbinary, and gender-diverse individuals.

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)0.007
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.039
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.007
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.064
GPT teacher head0.337
Teacher spread0.273 · 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 designSystematic review
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

Citations21
Published2022
Admission routes2
Has abstractyes

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Same venueAmerican Journal of Physiology-Heart and Circulatory PhysiologySame topicSex and Gender in HealthcareFrench-language works237,207