Health implications and management of women with opioid use disorder
Bibliographic record
Abstract
Opioid use disorder has risen to epidemic proportions in the United States at an alarming rate in the past decade and is considered a leading public health concern. Women have a higher rate of acute and chronic pain conditions and are more likely to be prescribed opioids for pain management. The disproportionate incidence of opioid use, misuse, and progression to heroin and injectable drug use among reproductive age women is associated with increased morbidity and mortality. Of particular concern are the unique health risks opioid-dependent women face including immune system alterations, endocrinopathies, diminished fertility, psychosocial isolation, interpersonal violence, and unintentional overdose. Opioid use in pregnancy is associated with negative maternal and neonatal consequences and requires comprehensive, multidisciplinary services for the co-occurring medical, mental health, infectious disease, social stressors, and legal issues. Neonatal abstinence syndrome is linked to a cluster of physiological withdrawal symptoms and considered the primary adverse outcome of opioid exposure in newborns. Maternal medication-assisted treatment with methadone or buprenorphine to decrease the negative effects of neonatal withdrawal is the standard of care for opioid use disorders in pregnancy. The complexity of services required for maternal opioid use disorders requires collaborative and multidisciplinary management strategies to optimize maternal and neonatal 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".