Risk of Hypertensive Disorders of Pregnancy in Women Treated With Serotonin-Norepinephrine Reuptake Inhibitors
Bibliographic record
Abstract
Among antidepressants, serotonin-norepinephrine reuptake inhibitors (SNRIs) are particularly expected to increase the risk of hypertensive disorders of pregnancy (HDP) with regard to their biological mechanism. We aimed to evaluate the association between prenatal exposure to SNRI and HDP. In EFEMERIS, a French database including pregnant women covered by the French Health Insurance System of Haute-Garonne (2004-2019), we compared the incidence of HDP among women exposed to SNRI monotherapy during the first trimester of pregnancy to the incidence among 2 control groups: (1) women exposed to selective serotonin reuptake inhibitor (SSRI) monotherapy during the first trimester and (2) women not exposed to antidepressants during pregnancy. We conducted crude and also multivariate logistic regressions. Of the 156,133 pregnancies, 143,391 were included in the study population, including 210 (0.1%) in the SNRI group, 1,316 (0.9%) in the SSRI group, and 141,865 (98.9%) in the unexposed group. After adjustment for depression severity and other mental conditions, the risk of HDP was significantly higher among women exposed to SNRIs (n = 20; 9.5%) compared to women exposed to SSRIs (n = 72; 5.5%; adjusted odds ratio [aOR] [95% CI] = 2.32 [1.28-4.20]) and to unexposed women (n = 6,224; 4.4%; aOR [95% CI] = 1.89 [1.13-3.18]). This study indicated an increased risk of HDP in women treated with SNRIs versus women treated with SSRIs.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".