Clinical Characteristics of Central European and North American Samples of Pregnant Women Screened for Opioid Agonist Treatment
Bibliographic record
Abstract
BACKGROUND: Little comparable information is available regarding clinical characteristics of opioid-dependent women from different countries. In the present study, women from the USA, Canada and a Central European country, Austria, screened for participation in the Maternal Opioid Treatment Human Experimental Research study, were compared with respect to their demographic and addiction histories. METHODS: Pregnant women (n = 1,074) were screened for study participation using uniformed clinical criteria and instruments. The screening results were compared with regard to exclusion, demographics, drug use, and psychosocial and treatment histories. RESULTS: Compared to the screened US and Canadian women, Austrian women were more likely to be younger (p < 0.001), white (p < 0.001), had significantly lower levels of educational attainment (p < 0.001), were less likely to use opioids daily (p < 0.001) and more likely to have been prescribed buprenorphine (p < 0.001). Compared to both rural and urban US groups, the Austrian group was less likely to have legal issues (p < 0.001) and was younger when first prescribed agonist medication (p < 0.001). CONCLUSION: The differences between North American and European groups may offer unique insights concerning treatment and pregnancy outcomes for opioid-dependent pregnant women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".