Comparison of Body Image Perception and Depression in Polycystic Ovarian Syndrome (PCOS) and Non-PCOS Women
Bibliographic record
Abstract
Objectives: The aim of the current study was to appraise the relationship between women’s body image perception and depression in case and controls. Methods: In this case-control study 60 polycystic ovarian syndrome patients established agreeing to the Rotterdam criteria and 60 healthy controls of reproductive age group were enrolled. The PCOS patients and healthy controls were evaluated on questionnaire for physical appearance and depression. Body image perception was accessed using the validated Body Esteem Scale. The symptoms of Depression were evaluated with the Quick Inventory of Depressive Symptomatology-Self Report. Results: 55% of PCOS patients had depression while 36.7% were found with depression in the control group. In the PCOS group 65% of patients were found with positive body image while 98.3% of patients were found with positive body image. Significant association of study cases group was found with BMI group (p=0.049), diet habit (p=0.013), depression (p=0.044) and body image (p=0.000). Patients with depression are also more likely to have PCOS in comparison with those who haven’t (OR=2.111). Conclusion: There was a significant association of study group with body image perception and depression. Therefore, health of the patients with this set of symptoms is essential to be acknowledged more fully, predominantly in relation to the despair and poor body image. The outcomes of this study foster implications for clinical practice and propose that a multidisciplinary team should be involved in treatment of PCOS. Key words: Polycystic Ovary Syndrome (PCOS), Quick Inventory of Depressive Symptomatology (QIDS), Depression.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".