A systematic review investigating prenatal cannabis and tobacco co-exposure: Impacts on neonatal, behavioral, cognitive and physiological outcomes
Bibliographic record
Abstract
Background: Despite the high and increasing rates of cannabis and nicotine/tobacco product (NTP) use during pregnancy, the impact of their combined use on health outcomes in offspring remains poorly understood. Given the growing body of research on prenatal cannabis and NTP co-exposure and its effects on neonatal, behavioral, cognitive, and physiological outcomes in offspring, we conducted a systematic review to synthesize the existing literature and evaluate whether prenatal co-exposure results in additive and/or synergistic adverse effects compared to prenatal cannabis-only exposure and prenatal NTP-only exposure. Methods: We searched Medline, Embase, and PsycINFO databases via OVID for human and animal studies examining the association between prenatal co-exposure and single-substance exposure on neonatal, behavioral, cognitive, and physiological outcomes in offspring. Results: Of 3217 records identified, 46 articles were included in the review (human, n = 43; preclinical n = 3). For select neonatal outcomes, co-exposed infants exhibited a higher risk of compromised physical development and birth defects relative to infants with single-substance exposure. Behavioral outcomes, particularly emotion regulation/reactivity, and physiological outcomes demonstrated a similar pattern. In contrast, other neonatal outcomes (e.g., preterm birth and respiratory distress), and cognition were similar between the prenatal co-exposure and single-substance exposure groups. Conclusions: This review suggests additive and/or synergistic adverse consequences associated with co-exposure on several outcomes in offspring relative to single substance exposure. These findings highlight the urgent need for prevention and treatment strategies addressing cannabis and NTP use in pregnant women. We discuss the limitations of the included studies and highlight key areas for 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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".