Effects of opioid use in pregnancy on pediatric development and behaviour in children older than age 2: Systematic review.
Bibliographic record
Abstract
OBJECTIVE: To summarize information on the effects of opioid use in pregnancy on subsequent pediatric development and behaviour. DATA SOURCES: Searches were performed in EMBASE, MEDLINE, and PsycINFO for peer-reviewed, English articles, including a manual search of their references, that were published between January 1, 2000, and May 1, 2018. STUDY SELECTION: Of the 543 articles reviewed, 19 relevant articles that focused on developmental effects of opioid exposure in utero were identified. Most of the studies provided level II evidence. One level I meta-analysis and 1 level III expert committee report were included. SYNTHESIS: The literature was divided between documenting some level of impairment or normalization of early development deficits over time. Often no opioid effect was found once researchers controlled for socioenvironmental factors. The degree to which environmental factors, opioid exposure, or both affect pediatric development remains to be determined. CONCLUSION: The effect of maternal opioid use on pediatric development is unclear and the evidence is inconsistent. However, opioid exposure in pregnancy does define these children as a population at risk. They might experience developmental delays compared with their peers, yet remain within population norms in cognition, fine-motor skills, hand-eye coordination, executive function, and attention and impulsivity levels.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".