Obstetrical and Neonatal Outcomes in Pregnancies Affected by Cannabis Abuse or Dependence [36E]
Bibliographic record
Abstract
INTRODUCTION: Cannabis is one of the most commonly used illicit drugs during pregnancy in the United States and this prevalence may rise with a growing number of states legalizing its use. We aimed to describe the rate of its use during pregnancy and evaluate its adjusted effect on obstetric and neonatal outcomes. METHODS: We performed a retrospective population-based cohort study composed of 12 million births in the United States between 1999 and 2013, extracted from the National Inpatient Sample, compiled by the Healthcare Cost and Utilization Project. Births to mothers abusing or dependent on cannabis were identified using ICD-9 codes and were compared to unexposed women. The adjusted effect of cannabis exposure during pregnancy on various obstetrical and neonatal outcomes was assessed with the use of unconditional logistic regression. RESULTS: There were 66,925 births to mothers using cannabis with an incidence rising from 0.28% in 1999 to 0.95% in 2013, p<.05. Compared with the reference population, women reporting cannabis use were at a significantly higher risk of having a preterm premature rupture of membranes (OR 1.46, 95%CI 1.35-1.58) and miscarriage (OR 1.46 95% CI 1.34-1.58). Neonates born to exposed mothers were at a higher risk of being born prematurely (OR 1.40 95% CI 1.36-1.43) and growth restricted (OR 1.35 95%CI 1.30-1.41). CONCLUSION: Cannabis use during pregnancy has steadily increased over the study period and is associated with important adverse newborn outcomes. With legalization of cannabis use for recreational purposes, pregnancy may become an important public health concern and should be monitored.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".