Modifiable predictors to maternal visceral adipose tissue during pregnancy: A clinical, demographic, and nutritional study
Bibliographic record
Abstract
AIM: Higher amounts of maternal visceral adipose tissue were related to abnormal outcomes in pregnancy. Our objective was to evaluate the impact of modifiable and nonmodifiable predictors related to abnormal amounts of maternal visceral fat during three trimesters of pregnancy. METHODS: Visceral fat thickness was evaluated by ultrasound during three trimesters centered in the maternal epigastrium (preperitoneal m-VAT) and additionally fat thickness evaluation centered at maternal periumbilical region (periumbilical m-VAT) among cases with gestational age below 20 weeks. The fourth quartile was considered abnormal m-VAT and the first three quartiles as normal m-VAT. Nonmodifiable characteristics included maternal age, past term pregnancies, and ethnicity. Modifiable characteristics included pre-pregnancy body mass index (BMI), weight gain, usual macronutrients, and sugar consumption during pregnancy. RESULTS: Preperitoneal m-VAT was assessed in 270 pregnant women and m-VAT periumbilical assessment in 154. The fourth quartile measurement was 15 mm and 53 mm, respectively. Nonmodifiable predictors including maternal age and past term pregnancies significantly impacted the primary study outcome of abnormal periumbilical m-VAT. Having a non-Caucasian ethnicity had a significant impact on the amount of normal preperitoneal m-VAT. Among the modifiable characteristics, both pre-pregnancy BMI and pre-pregnancy obesity impacted the amount of abnormal preperitoneal and periumbilical m-VAT. CONCLUSION: Abnormal amounts of maternal visceral fat during pregnancy are related to nonmodifiable predictors and those present before pregnancy. No impact was found among weight gain during pregnancy or macronutrients and sugar consumption at pregnancy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".