Polydrug use among pregnant methadone users
Bibliographic record
Abstract
Methadone maintenance treatment (MMT) is used to manage opioid dependence. However, the common practice of polydrug use poses a serious concern with women co‐abusing substances during pregnancy. The objective of this study was to assess polydrug use in pregnant methadone users by meconium result analysis. Meconium samples were collected from the Motherisk Laboratory for testing as per requests by children's aid societies and hospitals. Over 22 months, samples were tested for substances of abuse through immunoassay (ELISA) or GC‐MS analysis. All methadone positive samples were used to assess frequency of polydrug use in a high‐risk pregnant population with methadone negative samples serving as controls. Of the tested samples, 117 were positive and 88 were negative for methadone. Opioids were the most prevalent drug class detected for both groups (44.4 and 50% respectively). No statistical difference was found for the prevalence of any individual or class of drug, with the exception of codeine (p=0.042), nor for the average minimum number of drugs detected (1.33 (SD=1.05) and 1.55 (SD=1.30) respectively). With opioids being the most prevalent drug class, this could suggest that opioid dependence is not effectively managed in a substantial fraction of individuals on MMT. With a high rate of polydrug use, it is pertinent to study the effects this can have on the fetus in future research.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".