The Impact of Testosterone Therapy on Cardiovascular Risk Among Postmenopausal Women
Bibliographic record
Abstract
Purpose: To summarize the current state of knowledge surrounding the impact of testosterone therapy on cardiovascular risk factors in postmenopausal women. Methodology: In this scoping review, a comprehensive search of peer-reviewed literature was conducted in adherence to a methodological framework comprising 4 distinct stages: conceptualizing a comprehensive search strategy, screening relevant publications, extracting pertinent data, and organizing and synthesizing the resultant findings. The search used electronic databases, including MEDLINE, Embase, and Google Scholar, to ensure an exhaustive survey of the available literature. Results: The database search yielded 150 articles, including systematic reviews, registered trials, and peer-reviewed studies, of which 48 duplicates were removed. Following the title/abstract screening, 36 publications were included in the full-text review. On completion of the full-text review, using the inclusion/exclusion criteria, 29 articles were excluded and 7 remained for data extraction and qualitative synthesis. Main Conclusion: Existing research provides promising insights into the benefits of low-dose testosterone therapy, typically combined with estrogen therapy. These benefits may include positive impacts on body composition, functional capacity, insulin sensitivity, inflammatory markers, and cholesterol. However, there remains a substantial lack of knowledge surrounding the effects and mechanisms behind testosterone therapy in postmenopausal women in relation to its impacts on cardiovascular risk. High-quality, evidence-based clinical intervention research is needed to investigate testosterone therapy's potential implication on cardiovascular risk factors in post-menopausal women.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".