Prevalence and Etiology of Eating Disorders in Polycystic Ovary Syndrome: A Scoping Review
Bibliographic record
Abstract
Polycystic ovary syndrome (PCOS) is the most common endocrine-metabolic disorder affecting females across the lifespan. Eating disorders (EDs) are psychiatric conditions that may impact the development of PCOS and comorbidities including obesity, metabolic syndrome, and type 2 diabetes. The aim of this scoping review was to determine the prevalence of EDs and disordered eating, and to review the etiology of EDs in PCOS. The review was conducted using search terms addressing PCOS, EDs, and disordered eating in databases, including PubMed, Scopus, PsycINFO, and CINAHL. Structured interviews, self-administered questionnaires, chart review, or self-reported diagnosis were used to identify EDs in 38 studies included in the review. The prevalence of any ED in those with PCOS ranged from 0% to 62%. Those with PCOS were 3-6-fold more likely to have an ED and higher odds ratios (ORs) of an elevated ED score compared with controls. In those with PCOS, 30% had a higher OR of bulimia nervosa and binge ED was 3-fold higher compared with controls. Studies were limited on anorexia nervosa and other specified feeding or ED (such as night eating syndrome) and these were not reported to be higher in PCOS. To our knowledge, no studies reported on avoidant/restrictive food intake disorder, rumination disorder, or pica in PCOS. Studies showed strong associations between overweight, body dissatisfaction, and disordered eating in PCOS. The etiologic development of EDs in PCOS remains unclear; however, psychological, metabolic, hypothalamic, and genetic factors are implicated. The prevalence of any ED in PCOS varied because of the use of different diagnostic and screening tools. Screening of all individuals with PCOS for EDs is recommended and high-quality studies on the prevalence, pathogenesis of specific EDs, relationship to comorbidities, and effective interventions to treat ED in those with PCOS are 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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.017 |
| 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".