Opioid dependence during pregnancy: relationships of anxiety and depression symptoms to treatment outcomes
Bibliographic record
Abstract
AIMS: To examine the relationship of anxiety and depression symptoms with treatment outcomes (treatment discontinuation, rates of ongoing use of illicit drugs and likelihood of preterm delivery) in opioid-dependent pregnant women and describe their use of psychotropic medications. DESIGN AND SETTING: Secondary data analysis from a randomized clinical trial of treatment for opioid dependence during pregnancy. PARTICIPANTS: A total of 175 opioid-dependent pregnant women, of whom 131 completed treatment. MEASUREMENTS: Symptoms of anxiety and depression were captured with the 15-item Mini International Neuropsychiatric Interview (MINI) screen. Use of illicit drugs was measured by urine drug screening. Preterm delivery was defined as delivery prior to 37 weeks' gestation. Self-reported use of concomitant psychotropic medication at any point during the study was recorded. FINDINGS: Women reporting only anxiety symptoms at study entry were more likely to discontinue treatment [adjusted odds ratio (OR) = 4.56, 95% confidence interval (CI) : 1.91-13.26, P = 0.012], while those reporting only depression symptoms were less likely to discontinue treatment (adjusted OR = 0.14, 95% CI : 0.20-0.88, P = 0.036) compared to women who reported neither depression nor anxiety symptoms. No statistically significant between-group differences were observed for ongoing illicit drug use or preterm delivery. A majority (61.4%) of women reported use of concomitant psychotropic medication at some point during study participation. CONCLUSIONS: Opioid agonist-treated pregnant patients with co-occurring symptoms of anxiety require additional clinical resources to prevent premature discontinuation.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".