Pregnancy outcomes following gabapentin use
Bibliographic record
Abstract
OBJECTIVES: Our objectives were to 1) determine whether first-trimester use of gabapentin is associated with an increased risk for major malformations; 2) examine rates of spontaneous abortions, therapeutic abortions, stillbirths, mean birth weight and gestational age at delivery; and 3) examine rates of poor neonatal adaptation syndrome following late pregnancy exposure. METHODS: The study design was prospective. Women were included who initially contacted the services between 5 and 8 weeks with a comparison group of women exposed to nonteratogens, collected in a similar manner. RESULTS: We have data on 223 pregnancy outcomes exposed to gabapentin and 223 unexposed pregnancies. The rates of major malformations were similar in both groups (p = 0.845). There was a higher rate of preterm births (p = 0.019) and low birth weight <2,500 g (p = 0.033) in the gabapentin group. Among infants who were exposed to gabapentin up until delivery, 23 of 61 (38%) were admitted to either the neonatal intensive care unit or special care nursery for observation and/or treatment, vs 6 of 201 (2.9%) live births in the comparison group (p < 0.001). There were 2 cases of possible poor neonatal adaptation syndrome in neonates exposed to gabapentin close to delivery, compared with none in the comparison group, although it must be noted that these infants were concomitantly exposed to other psychotropic drugs. Among the women who took gabapentin, the major indications were pain (n = 90; 43%) and epilepsy (n = 71; 34%); the remainder were for other indications, mostly psychiatric. CONCLUSION: Our results suggest that although this sample size is not large enough to make any definitive conclusions, and there was no comparator group treated with other antiepileptic drugs, gabapentin use in pregnancy does not appear to increase the risk for major malformations. This finding and the increased risk for low birth weight and preterm birth require further investigation.
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".