Assessing dutasteride‐associated sexual dysfunction using the U.S. Food and Drug Administration Adverse Event Reporting System
Bibliographic record
Abstract
BACKGROUND: Incidences of sexual dysfunction due to the use of 5 α-reductase inhibitors have been suggested. Despite low sexual dysfunction reported in clinical trials, an analysis of the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database revealed a significant disproportionality in the reporting of sexual dysfunction with the use of finasteride. Therefore, it is likely that a similar relationship with dutasteride may exist. OBJECTIVE: To determine whether dutasteride use leads to a higher risk of sexual dysfunction compared to a baseline risk for all other drugs using the 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. Cases of dutasteride-associated sexual dysfunction were compared to a reference risk of sexual dysfunction for all other drugs in the database. RESULTS: A significant disproportionality in reporting of sexual dysfunction with the use of dutasteride was observed. The disproportionality was present for all age ranges except for 31-45 years where there were few overall reports of adverse events. LIMITATIONS: Adverse events can be underreported, and selection bias is inherent in the FAERS. CONCLUSION: Dutasteride use is associated with an increase in reports of sexual dysfunction.
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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 | 0.030 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".