Anti-epileptic drug exposure during pregnancy and neonatal birth weight outcomes: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The prevalence of epilepsy in pregnant women is estimated at 0.3-1%. Anti-epileptic drug (AED) exposure in-utero has been associated with various adverse health outcomes in neonates, including adverse birth weight outcomes. OBJECTIVE: This review aims to summarize the published evidence on the association between AED exposure in pregnancy and adverse birth weight outcomes METHODS: Studies assessing AED exposure in pregnancy and neonatal birth weight outcomes, including small for gestational age (SGA), low birth weight (LBW), birth weight (BW), length, head circumference, and cephalization index will be identified in MEDLINE®, EMBASE, Cochrane Library, Scopus, Cumulative Index of Nursing and Allied Health Literature (CINAHL), International Pharmaceutical Abstracts (IPA), and Global Health. Open grey, Theses Canada, and ProQuest Dissertations will be used to locate gray literature. Eligible study designs will include both intervention and non-interventional studies. We will not impose any time limit in the review. We will use the Newcastle-Ottawa Scale to assess the methodological quality of observational studies and quasi-experimental studies included in the review. The risk of bias of experimental studies will be appraised using the Cochrane risk-of-bias tool for randomized trials (RoB 2). A meta-analysis will be conducted using a random-effects model. DISCUSSION: The results from this review could improve clinicians' prescribing decisions by highlighting the safest AEDs for women who are pregnant or planning to conceive based on the evidence currently available. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020192713.
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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 | 0.063 | 0.085 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.025 | 0.033 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.072 | 0.007 |
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".