Risk for postpartum depression associated with assisted reproductive technologies and multiple births: a systematic review
Bibliographic record
Abstract
BACKGROUND: It has been hypothesized that certain obstetrical populations, including women who conceive using assisted reproductive technologies (ART) and women with multiple births, may be at increased risk for postpartum depression. In this systematic literature review, we examine the published evidence for this hypothesis. METHODS: The databases Medline, CINAHL, EMBASE, PsycINFO and the Cochrane Library were searched from their start dates through to April 1, 2009 using relevant keywords. All published, peer-reviewed articles in English, Spanish or French including a standardized assessment of depression administered between 2 and 52 weeks postpartum were considered for inclusion. Two independent reviewers abstracted and critically appraised a total of 13 eligible articles. RESULTS: The data indicate little or no increased risk for postpartum depression among women who use ART to conceive. In contrast, most studies of adequate quality indicate that mothers of multiples may be at elevated risk for symptoms of depression. However, existing data do not permit differentiation between transient maternal distress and clinically significant postpartum depression. CONCLUSIONS: Studies included in this review were often limited by small samples and lack of appropriate comparison groups, making further research in this area essential. In particular, lack of control for maternal psychiatric history and other important sociodemographic predictors of depression is a serious limitation of existing research on this topic. Further, the use of reproductive technologies and multiple births often co-occur, and few study designs enabled separation of the effects of these two variables. However, evidence of increased risk for symptoms of postpartum depression among women with multiple births, if confirmed, may warrant targeted interventions for this population.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 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".