Eating Disorders in Pregnant and Breastfeeding Women: A Systematic Review
Bibliographic record
Abstract
Background and objectives: Pregnancy is a stage associated with various biopsychosocial changes. These changes, along with concerns about keeping an adequate weight, can modulate an individual’s risk for psychological disorders, especially eating disorders (EDs). The aim of this review was to investigate the prevalence, associated risks, and consequences of eating disorders in pregnancy and in breastfeeding mothers. Materials and Methods: A systematic review was carried out following the PRISMA guidelines in the scientific databases: PubMed, Web of Science, Scopus, and PsycINFO. Search terms related to EDs, pregnancy, and breastfeeding were used. The evaluation of the methodological quality of the studies was carried out using different scales; CASP (Checklist for Cohort Study), NICE (Methodology Checklist for Cohort Study), ARHQ (Methodology Checklist for Cross-Sectional), and NOS (Newcastle-Ottawa Scale for Cohort). Results: From 2920 studies, 16 were selected to study EDs in pregnant women and 2 studies in nursing mothers. Most of the studies used questionnaires and scales as tools for the diagnosis of EDs. Binge eating, anxiety, and depression were the most common comorbidities of EDs, accompanied in most cases by excessive concern about weight gain. The consequences of EDs are diverse. The prevalence of EDs in this population is estimated to be 1 out of 20. Conclusions: Eating disorders are related to anxiety and depression and have negative consequences for both mothers and fetuses (cesarean, miscarriages, premature births). More research on the field to determine the risk factors for EDs in the population of pregnant and lactating women is 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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".