Features of nausea and vomiting in pregnancy as predictive factors of post-partum depression: a systematic review
Bibliographic record
Abstract
BACKGROUND: To investigate the association between nausea and vomiting during pregnancy (NVP), including hyperemesis gravidarum (HG), and postpartum depression (PPD), and to identify predictive symptoms of PPD to improve preventive care and follow-up. METHODS: A systematic search was conducted in six databases from inception to June 25, 2024, using predefined keywords and MeSH terms related to PPD and pregnancy-related vomiting. Eligible studies included original research articles examining the association between PPD and NVP (any duration or severity, including HG). Articles were restricted to English or French. Diagnostic criteria for NVP and PPD had to be clearly specified. The systematic review followed PRISMA guidelines and was registered with PROSPERO (CRD42024508214). Study quality was assessed using the Newcastle-Ottawa Scale (NOS). Two independent researchers conducted article screening, data extraction, and bias assessment. RESULTS: Out of 355 identified articles, 18 (mainly prospective and retrospective cohort studies) were included. Analysis revealed a significantly increased risk of PPD in women with NVP, ranging from adjusted odds ratios (aORs) of 1.54 [95% CI 1.42-1.68] for mild NVP to 6.52 [95% CI 3.76-11.30] for severe NVP and 2.26 [95% CI 1.04-4.92] for persistent symptoms. These values represent the lowest and highest aORs reported in individual studies, not a pooled meta-analytic estimate. The association remained significant in large-scale studies after adjusting for confounders. CONCLUSIONS: This review highlights the importance of monitoring the severity and duration of NVP during pregnancy. Identifying these symptoms should prompt systematic PPD screening, especially within the first 6 months postpartum, to facilitate timely intervention and improve maternal mental health outcomes. TRIAL REGISTRATION: 2024. PROSPERO registration number CRD42024508214.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| 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".