Management of obesity across women's life course: FIGO Best Practice Advice
Bibliographic record
Abstract
Obesity is a chronic, progressive, relapsing, and treatable multifactorial, neurobehavioral disease. According to the World Health Organization, obesity affects 15% of women and has long-term effects on women's health. The focus of care in patients with obesity should be on optimizing health outcomes rather than on weight loss. Appropriate and common language, considering cultural sensitivity and trauma-informed care, is needed to discuss obesity. Pregnancy is a time of significant physiological change. Pre-, ante-, and postpartum clinical encounters provide opportunities for health optimization for parents with obesity in terms of, but not limited to, fertility and breastfeeding. Pre-existing conditions may also be identified and managed. Beyond pregnancy, women with obesity are at an increased risk for gastrointestinal and liver diseases, impaired kidney function, obstructive sleep apnea, and venous thromboembolism. Gynecological and reproductive health of women living with obesity cannot be dismissed, with accommodations needed for preventive health screenings and consideration of increased risk for gynecologic malignancies. Mental wellness, specifically depression, should be screened and managed appropriately. Obesity is a complex condition and is increasing in prevalence with failure of public health interventions to achieve significant decrease. Future research efforts should focus on interprofessional care and discovering effective interventions for health optimization.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.012 |
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".