The association between birth weight and adulthood body fat mass in women with poly cystic ovary syndrome
Bibliographic record
Abstract
Introduction: Polycystic ovary syndrome (PCOS) is the most prevalent endocrinopathy in reproductive aged women. The association of early-life factors such as fetal adipose tissue and birth weight with adulthood outcomes like obesity, body fat mass (BFM) and body lean mass (BLM) is not clearly understood. We aimed to compare the association between birth weight and body composition in women with PCOS and normal controls. Materials and Methods: For this study we enrolled a total of 70 reproductive aged women with PCOS diagnosis, referring to the Reproductive Endocrinology Research Center and the same number of healthy women without polycystic ovaries by ultrasonography or hirsutism and/or anyovulatory other dysfunction were enrolled. Their birth weights were documented and their body composition was assessed, using standard measuring devices. Results: Cases were younger than the controls (29.7±4.9 versus 30.8±5.9 years) and had the same BMI (24.6±2.4 versus 24.8±4.5 kg/m2). Our study demonstrated that BFM and BLM are increased in adult PCOS women born underweight, compared to their normal counterparts (16± 4.7 versus 12.2± 4.1, P= 0.06 and 46.4±5.8 versus 41.1±5.8, P=0.07). Conclusion: The impact of fetal adipose tissue and birth weight on the occurrence adulthood obesity, BFM and BLM vary between women with and without PCOS.
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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 | 0.001 |
| 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".