Depression, Antidepressants and Hypertensive Disorders of Pregnancy: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Hypertensive disorders of pregnancy including gestational hypertension, preeclampsia and eclampsia are conditions that cause significant perinatal and maternal morbidity and mortality. OBJECTIVE: This is a systematic review of the current evidence examining the relationship between both depression and antidepressants on pregnancy-related hypertensive conditions. METHODS: In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, six databases were searched for articles published between January 1990 and December 2017 (PubMed, Embase, PsycINFO, Cochrane Database of Systematic Reviews, MEDLINE and ClinicalTrials. gov). Randomized control trials, cohort studies and case-control studies were included in this review. Studies that measured the following exposures were included: Antidepressant exposure or diagnosis of depression. Studies that measured the following outcomes were included: Gestational hypertension, preeclampsia or eclampsia. A combination of keywords, as well as Medical Subject Headings (MeSH) index terms, was used for three general categories: antidepressants, depression and hypertensive disorders of pregnancy. A total of 743 studies were identified and 711 were excluded based on relevance to the research question. Twenty studies were included in the final systematic review. RESULTS: Of the twenty relevant studies, ten specifically examined the relationship between depression and hypertension in pregnancy. Only two of these did not find a significant association. Of the ten studies that concentrated on antidepressant medications, all except one found an association with hypertension in pregnancy to varying degrees. CONCLUSION: Review of the literature suggests a possible association between depression and antihypertensive medications with pregnancy-related hypertension, but further studies are needed.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".