Postpartum hemorrhage and postpartum depression: A systematic review and meta‐analysis of observational studies
Bibliographic record
Abstract
OBJECTIVE: To assess the postpartum depression (PPD) risk in women with postpartum hemorrhage (PPH) and moderators. METHODS: We identified observational studies of PPD rates in women with versus without PPH in Embase/Medline/PsychInfo/Cinhail in 09/2022. Study quality was evaluated using the Newcastle-Ottawa-Scale. Our primary outcome was the odds ratio (OR, 95% confidence intervals [95%CI]) of PPD in women with versus without PPH. Meta-regression analyses included the effects of age, body mass index, marital status, education, history of depression/anxiety, preeclampsia, antenatal anemia and C-section; subgroup analyses were based on PPH and PPD assessment methods, samples with versus without history of depression/anxiety, from low-/middle- versus high-income countries. We performed sensitivity analyses after excluding poor-quality studies, cross-sectional studies and sequentially each study. RESULTS: = 98.9%). Higher PPH-related PPD ORs were estimated in samples with versus without history of depression/anxiety or antidepressant exposure (OR = 1.37, 95%CI = 1.18 to 1.60, k = 6, n = 55,212, versus 1.06, 95%CI = 1.04 to 1.09, k = 3, n = 879,220, p < 0.001) and in cohorts from low-/middle- versus high-income countries (OR = 1.49, 95%CI = 1.37 to 1.61, k = 4, n = 9197, versus 1.13, 95%CI = 1.04 to 1.23, k = 6, n = 925,235, p < 0.001). After excluding low-quality studies the PPD OR dropped (1.14, 95%CI = 1.02 to 1.29, k = 6, n = 929,671, p = 0.02). CONCLUSIONS: Women with PPH had increased PPD risk amplified by history of depression/anxiety, whereas more data from low-/middle-income countries are required.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".