Bibliographic record
Abstract
INTRODUCTION: Depression during pregnancy can affect up to 20% of all women and may be treated effectively with antidepressants. Currently, information on > 20,000 women exposed to antidepressants with pregnancy outcomes is available in the literature. However, there is a continuing fear of physicians prescribing and women taking these drugs during pregnancy, probably due to many of the studies reporting conflicting outcomes and subsequently, the dissemination of these results. AREAS COVERED: The authors searched the literature using Medline, Embase and Reprotox followed by a manual search of retrieved articles and reviews of the topic. The authors then selected key publications in this field which they considered relevant to the subsequent discussion of this topic. EXPERT OPINION: In this review, the authors evaluate the safety of different classes of antidepressants and find no convincing evidence of an increased risk for any adverse outcomes in an appreciable fashion. The authors note that even in studies documenting a potential for harm, the risk is marginal with rarely an odds ratio above 2. Therefore, it is important that each woman discusses the risks/benefits of treatment with her healthcare provider to allow an informed decision to be made based on scientific evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".