The overlooked mental health burden of polycystic ovary syndrome: neurobiological insights into PCOS-related depression
Bibliographic record
Abstract
Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder affecting 6-13% of reproductive-aged women worldwide. It is primarily characterized by ovarian dysfunction, hyperandrogenism, and metabolic disturbances. However, women with PCOS also face a heightened risk of depression, possibly due to dysregulation in endocrine and immune systems and gut microbiome disturbances. Symptoms of PCOS such as infertility, obesity, and hirsutism can also cause psychological distress and further exacerbate depression symptoms. Despite this comorbidity, mental health aspects of PCOS are often overlooked in the medical field, leading to insufficient support and negative impacts on the quality of life of PCOS patients. This review explores how distinct PCOS phenotypes influence physiological and psychological outcomes and the possible biological mechanisms involved. We also examine the effects of existing treatments on PCOS symptoms and depression. Addressing both physiological and psychological challenges is crucial for developing targeted, personalized interventions that improve outcomes for individuals diagnosed with PCOS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".