The Pivotal Influence of Obesity on Body Composition and Ovarian Doppler in Different Polycystic Ovarian Syndrome Phenotypes
Bibliographic record
Abstract
Aim: To investigate the effect of obesity on body composition and ovarian Doppler parameters in females with different polycystic ovarian syndrome (PCOS) phenotypes, and find the correlations between these parameters and PCOS clinical criteria. Design: Observational cross-sectional study. Methods: One hundred PCOS females participated in this study. We assessed the chronic anovulation (O), the hyperandrogenism (H), and polycystic ovaries (P) to classify the presence of PCOS phenotyping using the Rotterdam criteria into four types. They were classified according to their body mass index (BMI) into three groups. The normal weight group had BMI of 18.5-24.9 kg/m2 (n = 21); the overweight group had BMI of 25-29.9 kg/m2 (n = 39); the obese group had BMI of more than or equal to 30 kg/m2 (n = 40). Body composition parameters were evaluated by dual-energy X-ray absorptiometry (DEXA), while ovarian Doppler parameters were evaluated by colour Doppler ultrasound for all females in the three groups. Results: Obese PCOS females had significantly higher waist-hip ratio (WHR), total and regional fat mass, total and trunk lean mass, ovarian volume and blood flow velocities at both ovarian arteries than both normal weight and overweight PCOS females. The WHR was positively correlated to total fat and lean masses, as well as ovarian volume and blood flow velocities (P<0.05). The phenotype III and IV had significantly higher android & gynoid fat masses, total lean mass, also higher ovarian volume, PSV & EDV and significantly lower RI (P<0.05) comparing to type I and II. Conclusion: Obesity has a pivotal role in body composition distribution and ovarian Doppler parameters in different PCOS phenotypes.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".