Secondary Hypogonadism and Effects of Testosterone Replacement Therapy on Cardiovascular Events
Bibliographic record
Abstract
CONTEXT: The safety of testosterone replacement therapy (TRT) has generated some controversy during recent years. While untreated hypogonadism leads to diminished sexual characteristics, muscle weakness and osteoporosis, the cardiovascular safety of TRT has been vigorously debated. TRT remains the standard of care for those with secondary hypogonadism (SHG) due to structural pituitary disease, but long-term cardiovascular safety in this population remains unclear. METHODS: We conducted a retrospective cohort study to investigate occurrence of major adverse cardiovascular events (MACE) in male patients with nonfunctioning pituitary adenomas and prolactinomas, with and without SHG. Demographic data, TRT treatment, stroke, myocardial infarction, and mortality data were retrieved from chart review as well as provincial cardiac and stroke registries. RESULTS: There were 408 patients followed for a median 8.1 years (interquartile range 3.3-14.1); 150 (36.7%) did not have SHG, whereas 214 (52.5%) had SHG adequately treated with TRT and 44 (10.8%) had SHG that was untreated. Multivariable logistic regression analysis demonstrated no significant difference in MACE between groups. MACE outcomes were not impacted by size of adenoma, presence of other pituitary hormonal deficits, or testosterone levels. There was increased risk of mortality in untreated SHG compared to both TRT-treated SHG and those without SHG. CONCLUSION: TRT does not appear to increase risk of MACE in those with SHG related to pituitary disorders. Untreated SHG appears to convey increased risk of mortality though these patients were older and more comorbid.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".