Smoking in Pregnant Women Screened for an Opioid Agonist Medication Study Compared to Related Pregnant and Non-Pregnant Patient Samples
Bibliographic record
Abstract
BACKGROUND: Little is known about the prevalence and severity of smoking in pregnant opioid dependent patients. OBJECTIVES: To first characterize the prevalence and severity of smoking in pregnant patients screened for a randomized controlled trial, Maternal Opioid Treatment: Human Experimental Research (MOTHER), comparing two agonist medications; and second, to compare the MOTHER screening sample to published samples of other pregnant and/or patients with substances use disorders. METHODS: Pregnant women (N = 108) screened for entry into an agonist medication comparison study were retrospectively compared on smoking variables to samples of pregnant methadone-maintained patients (N = 50), pregnant opioid or cocaine dependent patients (N = 240), non-pregnant methadone-maintained women (N = 75), and pregnant non-drug-addicted patients (N = 1,516). RESULTS: Of screened patients, 88% (n = 95) smoked for a mean of 140 months (SD = 79.0) starting at a mean age of 14 (SD = 3.5). This rate was similar to substance use disordered patients and significantly higher compared to general pregnant patients (88% vs. 22%, p < .001). CONCLUSION AND SCIENTIFIC SIGNIFICANCE: Aggressive efforts are needed to reduce/eliminate smoking in substance-abusing pregnant women.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".