Assessing finasteride‐associated sexual dysfunction using the<scp>FAERS</scp>database
Bibliographic record
Abstract
BACKGROUND: Postmarketing reports suggest that finasteride causes sexual dysfunction despite a low incidence reported in clinical trials. Therefore, the extent of risk remains unknown. OBJECTIVE: To determine whether the risk of sexual dysfunction is higher among individuals treated with finasteride compared to a baseline risk for all other drugs using the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. METHODS: A case by non-case disproportionality approach was used whereby a reporting odds ratio (ROR) with 95% confidence interval (CI) was calculated. The National Ambulatory Medical Care Survey (NAMCS) was used to confirm results. RESULTS: A significant disproportionality in reporting of sexual dysfunction with the use of finasteride was observed whether finasteride was indicated for hair loss (ROR = 138.17, 95% CI: 133.13, 143.4), prostatic hyperplasia (ROR = 93.88, 95% CI: 84.62, 104.16) or any indication (ROR = 173.18, 95% CI: 171.08, 175.31). When these results were stratified by age, disproportionality was strongest at 31-45 years. CONCLUSION: Use of finasteride has led to an increase in reports of sexual dysfunction where it is believed to be the primary suspect.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".