Cognitive behavioural therapy for depression in women with PCOS: systematic review and meta-analysis
Bibliographic record
Abstract
Polycystic ovary syndrome (PCOS) is a common endocrine disorder with physical and psychological complaints, especially high depression scores. Cognitive behavioural therapy (CBT) is the first-line psychological treatment for depression. The objective of this study was to examine the effect of different types of CBT interventions and the effects on depression scores in women with PCOS. A literature search was performed in six databases up to July 2020. Studies published in English, in which depression scores were compared between groups during a CBT intervention in women with PCOS, were included. A total of 4854 studies were identified, of which eight studies were included in the systematic review and five in the meta-analysis. CBT ranged from 8 to 52 weeks and involved between 8 and 20 sessions. An overall Cohen's d effect size of 1.02 (95% confidence interval 0.02-2.02) was found in favour of CBT compared with standard care. To conclude, most psychological interventions applying CBT are effective in lowering depression scores in women with PCOS. These results should be interpreted with caution due to methodological differences and quality of the studies. More clinical trials are needed to assess how many sessions of CBT are necessary to treat depression in women 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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".