Exercise in the prevention and treatment of maternal–fetal disease: a review of the literature
Bibliographic record
Abstract
Evidence-based guidelines indicate that regular prenatal exercise is an important component of a healthy pregnancy. In addition to maintaining physical fitness, exercise may be beneficial in preventing or treating maternal-fetal diseases. Women who are the most physically active have the lowest prevalence of gestational diabetes (GDM), and prevention of GDM may decrease the incidence of obesity and type 2 diabetes in both mother and offspring. However, few studies have investigated the effectiveness of exercise in delaying or preventing GDM in at-risk women, and exercise prescriptions that optimize outcomes for women with GDM are lacking. Physically active women are also less likely to develop pre-eclampsia, and we have proposed the following 4 mechanisms that may explain this protective effect: enhanced placental growth and vascularity, reduced oxidative stress, reduced inflammation, and correction of disease-related endothelial dysfunction. Exercise may also prevent reproductive complications associated with maternal obesity. Obesity increases the risk of infertility and miscarriage, and weight loss programs that incorporate diet and exercise are a cost-effective fertility treatment that may also reduce the probability of obesity-related complications during pregnancy. Regular exercise following conception may prevent excessive gestational weight gain and reduce post-partum weight retention.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".