Obesity and Infertility: A Metabolic Assessment Strategy to Improve Pregnancy Rate.
Bibliographic record
Abstract
BACKGROUND: The metabolic global approach is a multidisciplinary intervention for obese women before undergoing assisted reproductive techniques, with the goal of improving fertility and decreasing adverse pregnancy outcomes. The objective of this study was to evaluate the impact of the metabolic global approach on pregnancy rate. METHODS: . Fertility treatments were considered when a weight loss of minimum 5% and normal metabolic indices were achieved. The p<0.05 was considered statistically significant. RESULTS: respectively (p<0.001), representing a median weight loss of 5.1%. At baseline, at least one metabolic parameter was abnormal in 66% of women. Total pregnancy rate was 53%. The majority of women (63%) who achieved pregnancy did so with weight loss and metabolic stabilization alone (11%) or combined with metformin (36%) and/or oral ovulation drugs (16%). Normal vitamin D (p<0.001) and triglyceride levels (p<0.05) as well as lower BMI after weight loss (p<0.05) were associated with an increased relative risk of pregnancy. CONCLUSION: Replete vitamin D status, weight loss of 5% and lower BMI as well as normal triglyceride level are significant and independent predictors of pregnancy in obese women presenting to our fertility center. The metabolic global approach is an effective program to detect metabolic abnormalities and improve obese women's pregnancy rate.
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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 | 0.001 |
| 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".