Selective Serotonin Reuptake Inhibitor Use During Pregnancy and Major Malformations: The Importance of Serotonin for Embryonic Development and the Effect of Serotonin Inhibition on the Occurrence of Malformations
Bibliographic record
Abstract
Bioelectric signaling is transduced by neurotransmitter pathways in many cell types. One of the key mediators of bioelectric control mechanisms is serotonin, and its transporter SERT, which is targeted by a broad class of blocker drugs (selective serotonin reuptake inhibitors [SSRIs]). Studies showing an increased risk of multiple malformations associated with gestational use of SSRI have been accumulating but debate remains on whether SSRI as a class has the potential to generate these malformations. This review highlights the importance of serotonin for embryonic development; the effect of serotonin inhibition during early pregnancy on the occurrence of multiple diverse malformations that have been shown to occur in human pregnancies; that the risks outweigh the benefits of SSRI use during gestation in populations of mild to moderately depressed pregnant women, which encompass the majority of pregnant depressed women; and that the malformations seen in human pregnancies constitute a pattern of malformations consistent with the known mechanisms of action of SSRIs. We present at least three mechanisms by which SSRI can affect development. These studies highlight the relevance of basic bioelectric and neurotransmitter mechanism for biomedicine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".