The Effect of Route of Testosterone on Changes in Hematocrit: A Systematic Review and Bayesian Network Meta-Analysis of Randomized Trials
Bibliographic record
Abstract
PURPOSE: We sought to compare testosterone formulations and determine the degree that hematocrit increases vary by testosterone therapy formulation. As head-to-head trials are rare, network meta-analysis of the contemporary studies is the only way to compare hematocrit changes by testosterone type, including topical gels and patches, injectables (both short-acting and long-acting) and oral tablets. MATERIALS AND METHODS: We conducted a thorough search of listed publications in Scopus®, PubMed®, Embase®, Cochrane CENTRAL, and ClinicalTrials.gov. A total of 29 placebo-controlled randomized trials (3,393 men) met inclusion criteria for analysis of mean hematocrit change after testosterone therapy. Randomized controlled trial data for the following formulations of testosterone were pooled via network meta-analysis: gel, patch, oral testosterone undecanoate, intramuscular testosterone undecanoate, and intramuscular testosterone enanthate/cypionate. RESULTS: All types of testosterone therapies result in statistically significant increases in mean hematocrit when compared with placebo. Meta-analysis revealed all formulations, including gel (3.0%, 95% CI 1.8-4.3), oral testosterone undecanoate (4.3%, 0.7-8.0), patch (1.4%, 0.2-2.6), intramuscular testosterone enanthate/cypionate (4.0%, 2.9-5.1), and intramuscular testosterone undecanoate (1.6%, 0.3-3.0) result in statistically significant increases in mean hematocrit when compared with placebo. When comparing all formulations against one another, intramuscular testosterone cypionate/enanthate were associated with a significantly higher increase in mean hematocrit compared to patch, but no differences in hematocrit between other formulations were detected. CONCLUSIONS: All types of testosterone are associated with increased hematocrit; however, the clinical concern of this increase remains questionable, warranting future studies. This is the first network meta-analysis to quantify mean hematocrit change and compare formulations, given the absence of head-to-head trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".