Management of gestational weight gain in obese or overweight women based on resting energy expenditure: A pilot cohort study
Bibliographic record
Abstract
Resting energy expenditure (REE) comprises 60% of total energy expenditure and variations may be associated with gestational weight gain (GWG). This study aims to explore the usability and feasibility of REE guided intervention for GWG in obese and overweight women. We conducted a prospective cohort study in LuHe Hospital of Capital Medical University in Beijing, China between May 1, 2017 and May 31, 2018. Obese/overweight women who had routine prenatal care visit at 10 to 13 weeks of gestation, were recruited after written informed consent was obtained. The intervention group (those women who were recruited between January 1 and May 31, 2018) used REE calculated daily total energy to manage GWG, while the control group (those women who were recruited between May 1 and December 31, 2017) used prepregnancy body mass index calculated daily total energy to manage GWG. GWG and daily total energy between the 2 groups were recorded from 10 to 13 weeks of gestation to delivery. A total of 68 eligible women (35 in intervention group and 33 in control group) were included in the final analysis. Daily total energy in the intervention group increased less than the control group, especially from 2nd trimester to 3rd trimester (1929.54 kcal/d vs. 2138.33 kcal/d). The variation of daily total energy from 1st trimester to 3rd trimester in the intervention group was lower than the control group (226.17 kcal/d vs 439.44 kcal/d). Overall GWG of the intervention group (13.45 kg) was significantly lower than the control group (18.20 kg). The percentage of excess-GWG in the intervention group (31.42%) was also significantly lower than the control (57.57%). Findings from our pilot study suggest that diet recommendation basting on REE may improve management of GWG in obese/overweight women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".