(270) WORLDWIDE INTEREST IN TESTOSTERONE REPLACEMENT THERAPY
Bibliographic record
Abstract
Abstract Introduction Testosterone replacement therapy (TRT) has garnered substantial attention in recent years as a potential solution to various health concerns associated with low testosterone levels. TRT addresses symptoms ranging from low energy and libido to muscle loss and mood disturbances. Interest in TRT varies across different geographical regions, and comprehending these distinctions may assist healthcare providers in tailoring the appropriate use of TRT. Objective This study aims to assess global interest in testosterone replacement therapy and explore its correlation with available health-related and socioeconomic data. Methods Google Trends was employed to gauge online-based public interest in TRT. The data from various countries were ranked and compared using correlation statistics with health-related and socioeconomic data from the World Health Organization and the World Bank. Results Interest in TRT, as indicated by Google Trends, has experienced a consistent increase over the past five years. The top five countries with the highest levels of interest were the United States, Australia, Canada, New Zealand, and the United Kingdom, respectively. Positive correlations were identified between the rank of TRT interest for each country and government healthcare expenditure (R = 0.517, 95%CI; 0.211, 0.731) as well as gross domestic product per capita (R = 0.581, 95%CI; 0.286, 0.776). However, there was no significant correlation between TRT interest and population life expectancy (R = 0.229, 95%CI −0.130, 0.535), the percentage of the population with internet accessibility (R = 0.225, 95%CI; −0.102, 0.555), density of medical doctors per population (R = 0.096, 95%CI; −0.261, 0.430), or the WHO universal health coverage index (R = 0.314, 95%CI; −0.039, 0.598). Conclusions The analysis reveals a rising trend of interest in TRT, particularly in countries with higher GDP and greater government healthcare expenditure. This heightened interest may be attributed to improved economic status and increased healthcare spending. These findings can inform healthcare policy implementation based on each country’s income level and facilitate the monitoring of inappropriate TRT usage within individual countries. Disclosure No.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.017 |
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".