The Effects of opioids on female fertility, pregnancy and the breastfeeding mother‐infant dyad: A Review
Bibliographic record
Abstract
Opioids cover a broad class of natural, synthetic and semi-synthetic drugs that act on opioid receptors to produce powerful analgesic effects. Rates of opioid use and opioid agonist maintenance treatment have increased substantially in recent years, particularly among women. Trends and outcomes of opioids use on fertility, pregnancy and breastfeeding, and longer-term child developmental outcomes have not been well-described. Here, we review the existing literature on the health effects of opioid use on female fertility, pregnancy, breastmilk and the exposed infant. We find that the current literature is primarily concentrated on the impact of opioid use in pregnancy and neonatal outcomes, with little exploration of effects on fertility. Studies are limited in number, some with small sample sizes, and many are hampered by methodological challenges related to confounding and other potential biases. Opioid use is becoming more prevalent due to environmental pressures such as COVID-19. More research is needed to better elucidate its effects on reproductive health among younger women and support the development of evidence-based recommendations for safe prescription practices and public health messaging.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".