In utero opioid exposure and birth outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background The nonmedical use of opioids during pregnancy, either those prescribed or obtained illegally, is a worldwide public health issue. Opioids pass through the placental barrier, which can expose the fetus to maternal opioid use; opioid-exposed babies may experience Neonatal Abstinence Syndrome (or Neonatal Opioid Withdrawal). We conducted a systematic review and meta-analysis to investigate how maternal opioid use during pregnancy may impact infants' birth outcomes. Methods We searched PubMed, Embase, PsycInfo, and the Web of Science, and identified 90 articles that met our inclusion criteria. Cohort, case-control and cross-sectional studies comparing birth outcomes of any opioid-exposed group (prescribed or obtained illegally) and a non-exposed comparison group were eligible for our systematic review. An adapted version of the Newcastle-Ottawa-Scale was used for quality assessment of the studies. Due to high heterogeneity between studies, we used random effects models to estimate pooled effects. Results In meta-analyses, opioid-exposed infants had lower birthweight (mean difference (MD):-405.9 grams, 95%CI: -472.26,-339.54, N = 37 studies), smaller head circumference (MD:-1.19 cm, 95%CI:-1.41, -0.96,N=22 studies), shorter gestational age (MD:-0.93 weeks, 95%CI: -1.20, -0.66,N= 34 studies), and shorter birth length (MD:-0.97 cm, 95%CI: -1.20, -0.74, N = 15 studies). The pooled relative risk of fatal outcomes was higher among the exposed infants: (RR:2.64, 95%CI: 1.06,6.59). Almost half of the studies were rated as poor, based on the Newcastle Ottawa Scale. Conclusions Our meta-analysis provides evidence of a link between opioid use during pregnancy and multiple adverse infant birth outcomes. Efforts should focus on increasing awareness about risks associated with opioid use and provide access to harm reduction measures for people who use opioids preconceptionally and in pregnancy. Key messages This meta-analysis provides insight into the magnitude of effects of in-utero opioid exposure on birth outcomes among the included studies. The findings highlight the importance of access to harm reduction measures in the periconceptional period and during pregnancy for people who use opioids.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".