A cross-sectional comparison of secondary polycythemia in testosterone-deficient men treated with nasal testosterone gel vs. intramuscular testosterone cypionate
Bibliographic record
Abstract
Introduction: Secondary polycythemia is a known adverse effect of testosterone replacement therapy (TRT). Different testosterone formulations are available, with significantly different half-lives, which have varying influences on the development of secondary polycythemia. Herein, we compared the prevalence of secondary polycythemia in testosterone-deficient men treated with intranasal testosterone gel (Natesto) vs. intramuscular testosterone cypionate (TC) therapy. Methods: We performed a cross-sectional analysis of secondary polycythemia (hematocrit [Hct] ≥54%) in men who received TRT. We included a total of 60 men: 30 men who received Natesto (4.5% testosterone gel [tid, 5.5 mg/nostril, 11 mg/dose, 33 mg/day]), and 30 who received TC (between 0.5 and 1.0 mL or 100–200 mg intramuscularly weekly). A univariable and multiple regression analysis was performed considering last Hct measurement as the main outcome. The analyzed variables included were age, body mass index (BMI), smoking history, treatment group, and testosterone levels on followup. Results: We identified polycythemia (Hct ≥54%) in 10% (3/30) of men who received TC. Additionally, in men treated with TC, 33.3% (10/30) had a Hct ≥50% during therapy. None of the men who received Natesto had a Hct ≥50% during therapy. On multivariable linear regression analysis, we demonstrated that the use of TC increased Hct by 3.24% (95% confidence interval [CI] 0.74–5.73%; p=0.012) compared to Natesto. Conclusions: The prevalence of polycythemia in men treated with Natesto was markedly lower compared to the men who received TC therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".