Androgen Treatment of Depressive Symptoms in Older Men: A Systematic Review of Feasibility and Effectiveness
Bibliographic record
Abstract
OBJECTIVE: To determine the feasibility and effectiveness of androgen treatment of depressive symptoms in older men. METHOD: We searched MEDLINE, PsycINFO, Eric, HealthStar, and the Cochrane Database of Systematic Reviews for potentially relevant articles. The bibliographies of relevant articles were searched for additional references and experts were consulted. Seventeen reports (8 open and 12 randomized trials; 3 articles reported studies of 2 designs) met the following inclusion criteria: original investigation; published in English or French, use of acknowledged criteria or scale for depression, and open or randomized trial of androgen treatment. Four criteria assessed study validity: randomization, double blinding, comparability of treatment and control groups at baseline, and description of dropouts. We abstracted, tabulated, and compared information from each article. RESULTS: Most studies had methodological limitations. With regard to feasibility, there were few reported withdrawals due to adverse events. With regard to effectiveness, 6 of 8 open trials had positive results, and 5 of 12 randomized trials had positive results, although 1 was equivocal. However, testosterone was combined with antidepressant medication in 3 of these positive randomized trials. Only 2 open trials enrolled subjects whose mean age was 60 years or over. One had positive results, and the other had negative results. CONCLUSION: Androgen therapy may be feasible in the short term, but there is little evidence that it is an effective treatment for depressive symptoms in older men.
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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 | 0.018 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".