Opioid Analgesics Suppress Male Gonadal Function, but Opioid Use in Men and Women Does Not Correlate with Symptoms of Sexual Dysfunction
Bibliographic record
Abstract
BACKGROUND: Opioid analgesia impairs gonadal function in men and women, but the correlation with symptoms and hormonal measurements of hypogonadism is not well established. OBJECTIVE: To determine the frequency of impaired gonadal function in men and women using opioids for chronic pain, and to determine the correlation of symptoms with hormonal measurements of gonadal function. METHODS: A prospective study of patients attending a multidisciplinary pain clinic was conducted. A total of 65 women (47 opioid users and 18 nonopioid analgesic controls) and 32 men (26 opioid users and six controls) were enrolled. Histories of sexual dysfunction and hormonal testing (men: total testosterone [TT], free testosterone [FT], prolactin and luteinizing hormone; women: FT, TT, prolactin, dehydroepiandrosterone sulphate, sex hormone- binding globulin, progesterone, luteinizing hormone and follicle- stimulating hormone, and estradiol) were obtained. RESULTS: In men, a low FT level was more common in opioid users (20⁄26; P=0.04). In men with abnormal hormone levels, there was no difference in the frequency of sexual dysfunction compared with men with normal hormone levels, and no difference in the frequency of opioid versus nonopioid use. In women, opioid users had lower FT levels (P=0.02). Low dehydroepiandrosterone sulphate was more frequent in women on opioids (P=0.03) in the menopausal group only (P=0.046). Premenopausal women taking opioids more frequently had a low TT level (P=0.03). The frequency of female sexual dysfunction was the same in opioid users (32⁄47) and controls (13⁄18; P=0.75), and also did not relate to any hormone abnormality. DISCUSSION: Men taking opioids had lower FT and higher prolactin levels, and women taking opioids had lower FT levels. Frequency of sexual dysfunction did not correlate with hormone levels in either men or women taking opioids. CONCLUSION: Opioids frequently cause low FT levels in men, but there is no relationship between abnormal hormone levels and symptoms of sexual dysfunction. Therefore, all men should be screened for low FT levels. Women on opioids had lower FT levels, but this did not correlate with sexual dysfunction symptoms. Therefore, measurements of FT or other hormones were not considered to be useful in women.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".