The Effect of Maternal Physical Activity and Gestational Weight Gain on Placental Efficiency
Bibliographic record
Abstract
INTRODUCTION: Adherence to physical activity (PA) and gestational weight gain (GWG) recommendations during pregnancy has been shown to improve maternal and fetal health outcomes, including reducing the risk for chronic diseases. Limited research has evaluated the effect of meeting PA in combination with GWG recommendations on placental efficiency (Pl-E), a surrogate marker of the placenta's ability to exchange nutrients and gas based on surface area. The purpose of this study was to measure and compare Pl-E based on meeting PA and GWG recommendations. METHOD: Healthy pregnant women (n = 61) wore accelerometers in their second and third trimesters to objectively measure PA. Women were classified as active or inactive at each time point based on meeting the 2019 Canadian prenatal PA guidelines. Total GWG was calculated as weight measured in the third trimester minus self-reported prepregnancy weight, and were categorized as insufficient (n = 19), adequate (n = 22), and excessive (n = 20) according to the 2009 Institute of Medicine guidelines. Placental weight (PW) and birth weight (BW) were measured within 30 min of delivery and 24-48 h postdelivery, respectively. Pl-E was determined in three ways: BW:PW ratio, residual BW, and measured BW, with a higher value indicating better Pl-E. Pl-E was compared by PA and GWG status using a two-way ANOVA. RESULTS: No differences were found in the BW:PW ratio or residual BW corresponding to PA and GWG status. Measured BW was significantly higher in newborns of women who gained weight excessively compared with those who gained insufficient weight (P < 0.05). CONCLUSION: These findings suggest that prenatal PA does not compromise Pl-E; however, further research is required to evaluate the potential mechanistic benefits of meeting PA and GWG guidelines on the placenta.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".