An Internet‐Based Survey to Assess Clinicians’ Knowledge and Attitudes Towards Opioid‐Induced Hypogonadism
Bibliographic record
Abstract
BACKGROUND: Long-term opioid therapy for chronic pain management requires regularly assessing and documenting benefits and side effects. Opioid-induced sex hormone disturbances are a complication that needs to be assessed routinely and perhaps not only when suspected. There is abundant literature about its prevalence, clinical consequences, and treatment, yet routine hormone screening and appropriate treatment are seldom performed in pain clinics. Ignorance, skepticism, and/or indifference are possible reasons explaining why opioid-induced hypogonadism (OIH) remains underdiagnosed among chronic pain patients. METHODS: This was an Internet-based survey reaching out to pain clinicians to assess their knowledge and attitudes regarding OIH. RESULTS: A total of 135 responses were received, representing a 23.7% response rate. Analysis of responses showed that 47% of responders were somewhat familiar with this complication, but their knowledge about the prevalence and the time to develop varied. Screening for OIH is ordered based on suspicion of its presence (50%), but not routinely (38%). Lack of knowledge was the most frequent reason adduced for not screening for OIH. Sex-related symptoms and signs are the most relevant reasons leading to suspicion and screening of OIH. Upon laboratory confirmation, most responders refer their patients to endocrinology (82%) for further management since most (60%) believe that testosterone replacement would improve their patients' health. CONCLUSIONS: Knowledge and attitudes towards OIH varied among this population of pain clinicians invited to participate in the research. Lack of knowledge and incertitude seem to impact the attitudes towards screening and treating OIH. Better medical training at undergraduate and postgraduate levels as well as continuous medical education may contribute to raising awareness about this complication and providing early treatment.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".