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Record W4280512729 · doi:10.1080/14779072.2022.2077193

Androgenic steroids dysregulation and the risk of coronary artery disease

2022· article· en· W4280512729 on OpenAlexaff
Abdulaziz Joury, Mona Alshehri, Luke Z. Li, Tameem Rezan

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2022
Typearticle
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineCoronary artery diseaseDyslipidemiaInternal medicineMyocardial infarctionTestosterone (patch)Clinical endpointVasospasmCardiologyAnabolic steroidDiseaseEndocrinologyRandomized controlled trialAnabolism

Abstract

fetched live from OpenAlex

INTRODUCTION: Endogenous testosterone deficiency or excess anabolic-androgenic steroids (AAS) have been linked to alter the physiology of different organs in the body, more specifically, the vasculature of coronary arteries. Despite the health-related concerns of using synthetic testosterone derivatives, such as AAS, there has been a tremendous increase in the use of AAS among athletes and bodybuilders. AREAS COVERED: We have highlighted the three main mechanisms that AAS increase the risk of coronary artery disease (CAD): altering the homeostasis of lipid metabolism which results in dyslipidemia and subsequently atherosclerosis, disturbing the function of platelet which results in platelet aggregation and subsequent thrombosis, and increasing the risk of coronary vasospasm by affecting the physiological function of vascular bed. EXPERT OPINION: Despite the restriction of AAS in specific clinical conditions such as testosterone deficiency and cancer therapy, many amateurs' athletes misuse the AAS. Although there has been a strong association between the AAS misuse and risk of developing CAD, the more valued approach would be a randomized clinical double-blind trial. The suggested primary endpoint would be an occurrence of adverse cardiovascular events, such as myocardial infarction, cerebrovascular accidents, and death. Increasing awareness of the risk of missing AAS among high-risk groups is imperative.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.958
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.013
GPT teacher head0.261
Teacher spread0.248 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2022
Admission routes1
Has abstractyes

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