Bibliographic record
Abstract
Obesity affects more than 35% of women aged 20 to 39 years in the United States. This article summarizes recent research that reconceptualizes obesity as adipose disease associated with smoking; socio-economic disparities in employment, education, healthcare access, food quality, and availability; and environmental toxins, ultimately altering microbiomes and epigenetics. Individual prenatal care of women with obesity includes early testing for diabetes, counseling on epigenetic diets, advice supporting weight gain within national guidelines, and vigilance for signs of hypertensive disorders of pregnancy. Intrapartum care includes mechanical cervical ripening measures, patience with prolonged labor, and uterotonic medication readiness in the event of postpartum hemorrhage. Postpartum care includes thrombus risk amelioration through early ambulation, use of compression stockings, and anticoagulation. Delays in lactogenesis II can be offset by measures to support early breastfeeding. Sociopolitical action by nurses at national, state, and community levels to reduce population disparities in racism, education, and employment; reduce pollution from obesogenic chemicals; and improve food quality and distribution policies is likely to have the broadest impact in future obesity reductions and prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".