Resting Energy Expenditure Guided Intervention for Gestational Weight Gain in Obese and Overweight Women
Bibliographic record
Abstract
Abstract BackgroundThere is sparse in the literature on resting energy expenditure guided intervention to manage gestational weight gain in obese and overweight women. Methods We conducted a prospective cohort study in Beijing, China between May 1, 2017 and April 30, 2018. Obese/overweight women who visited the Department of Obstetrics and Gynecology at LuHe hospital of Capital Medical University, a tertiary care facility in Beijing, China, for their routine prenatal care at 10-13 weeks of gestation during the study period were recruited into the study after written consent was obtained. Pregnant women who took steroid medication or who were diagnosed with thyroid disease or affected by pre-pregnant diabetes mellitus or for other reasons could not participate in the study assessments were excluded. Women who were recruited between May 1, 2017 and November 30, 2017 were the designated control group with diet recommendation based on pre-pregnancy body mass index and ideal weight, without resting energy expenditure monitoring. Women who were recruited between December 1, 2017 and April 30, 2018 were the intervention group, with resting energy expenditure guided diet recommendation to manage gestational weight gain. Gestational weight gain and perinatal outcomes between the two groups were compared.ResultsA total of 53 eligible women (32 in intervention group and 21 in control group) were recruited and included in the final analysis. There was no difference in baseline demographic and clinical characteristics between the two groups. Gestational weight gain (GWG) in the intervention group (13.45±4.16 Kg) was lower than in the control group (18.20±4.84 Kg). Rate of excess GWG in the intervention group (37.59%) was also lower than in the control group (66.67%). The REE in women of the intervention group with excess GWG increased faster than women with appropriate GWG during pregnancy, especially from 1st trimester to 2nd trimester. Rate of macrosomia in the intervention group (3.12%) was lower than in the control group (19.05%). There was no fetal growth restriction observed in either group. Conclusion: Resting energy expenditure guided nutrition intervention during pregnancy may lower GWG and lower incidence of macrosomia, with no apparent impact on fetal growth restriction. Future studies with larger sample size and randomized controlled trial design are needed to confirm findings from this small-scale study.
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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 | 0.000 | 0.001 |
| 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".