Opioid Detection in Maternal and Neonatal Hair and Meconium: Characterization of an At-Risk Population and Implications to Fetal Toxicology
Bibliographic record
Abstract
Identification of maternal opioid abuse in pregnancy is often difficult to ascertain in the absence of a reliable self-report. We aimed to characterize an at-risk neonatal population for opioid exposures as well as other drugs of abuse and alcohol. From June 2007 to January 2009, 563 neonatal hair and 1318 meconium specimens were assessed for opioids and were positive in 11.4% and 17.0%, respectively. Neonates testing positive for opioids in hair or meconium analysis were also more likely to test positive for other licit and illicit substances (odds ratiohair, 1.75; 95% confidence interval, 1.03-2.97; odds ratiomeconium, 1.61; 95% confidence interval, 1.16-2.22). Specifically, a positive neonatal hair test for opioids also predicted a positive result for oxycodone. In addition, a positive meconium test result for opioids was associated with positive results for cocaine, oxycodone, methadone, benzodiazepines, and fatty acid ethyl esters (alcohol). Finally, there was a significant correlation between maternal and neonatal hair test results for opioids (Spearman rank rho = 0.657, P = 0.03). Understanding the addiction profiles of these women may lead to better clinical and social management and may largely benefit an at-risk population.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".