Methadone induces testosterone suppression in patients with opioid addiction
Bibliographic record
Abstract
Sex hormones may have a role in the pathophysiology of substance use disorders, as demonstrated by the association between testosterone and addictive behaviour in opioid dependence. Although opioid use has been found to suppress testosterone levels in men and women, the extent of this effect and how it relates to methadone treatment for opioid dependence is unclear. The present multi-centre cross-sectional study consecutively recruited 231 patients with opioid dependence from methadone clinics across Ontario, Canada between June and December of 2011. We obtained demographic details, substance use, psychiatric history, and blood and urine samples from enrolled subjects. The control group included 783 non-opioid using adults recruited from a primary care setting in Ontario, Canada. Average testosterone level in men receiving methadone treatment was significantly lower than controls. No effect of opioids including methadone on testosterone level in women was found and testosterone did not fluctuate significantly between menstrual cycle phases. In methadone patients, testosterone level was significantly associated with methadone dose in men only. We recommend that testosterone levels be checked in men prior and during methadone and other opioid therapy, in order to detect and treat testosterone deficiency associated with opioids and lead to successful methadone treatment outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".