Highlighting the Mechanistic Relationship Between Perinatal Depression and Preeclampsia: A Scoping Review
Bibliographic record
Abstract
Background: Although there is scientific literature supporting an association between depression and preeclampsia (PE), little is known about the underlying mechanistic pathways that may explain these observed associations. Thus, this study aimed to outline the relationship between depression and PE, and to highlight the underlying cardiovascular and metabolic risk factors that are common to both. Methods: A scoping review of the literature was conducted in Medline, Scopus, and Web of Science. Results: From 706 articles initially identified, 23 articles met the inclusion criteria and were included in this review. Although some studies reported a positive association between PE and postpartum depressive symptoms, challenges comparing different methodologies, measurement instruments and when measurements were administered, and patient populations do not permit a decisive conclusion. In addition, very few studies addressed potential underlying mechanisms that may be contributing to observed associations; thus, a secondary search was conducted to identify cardiovascular and metabolic risk factors that are common to both depression and PE. Conclusion: increased inflammation and oxidative stress and decreased vascular and endothelial function) common to both depression and PE suggest that these factors may contribute as underlying mechanisms in both conditions. These similarities underscore the importance to better understand these mechanisms so preventative and therapeutic strategies could be developed to improve maternal health.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".