Resting energy expenditure guided intervention for gestational weight gain in obese and overweight women
Bibliographic record
Abstract
Abstract Background There 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. Women whose pre-pregnant body mass index was < 25Kg/m2 or who took steroid medication or those diagnosed with thyroid disease or affected by pre-pregnant diabetes mellitus or for other reasons could not participate in the study assessments were excluded. Participants who were recruited between May 1, 2017 and December 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. Those who were recruited between November 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 study groups were then compared.Results A 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 in the intervention group (13.45±4.16 Kg) was lower than in the control group (18.20±4.84 Kg). Rate of macrosomia in the intervention group (3.12%) was also lower than in the control group (19.05%). There was no fetal growth restriction observed in either group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".