Anthropometrical measurements and maternal visceral fat during first half of pregnancy: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Determining anthropometric measures that indicate different fat deposits can be useful to predict metabolic risk and set specific treatment goals, reducing negative consequences for maternal and fetal health. In cases where pre-gestational weight measure and subsequent body mass index (BMI) values cannot be determined, other anthropometric measurements may be ideal for measuring the nutritional status of pregnant women, especially in low- and middle-income countries. This study aims to identify which anthropometric measurements correlate better with the maternal fat deposits measured by ultrasound. METHODS: A cross-sectional study was conducted with pregnant women from the city of Porto Alegre (city), capital of Rio Grande do Sul (state), southern Brazil, from October 2016 until January 2018. Anthropometrical variables (weight, height, mid-upper arm circumference [MUAC], circumferences of calf and neck and triceps skinfolds [TSF] and subscapular skinfolds [SBSF]), and ultrasound variables (visceral adipose tissue [VAT] and total adipose tissue [TAT]) were collected. To verify the correlation of anthropometric and ultrasound measurements, a non-adjusted and adjusted Spearman correlation was used. The study was approved by the ethics committees. RESULTS: The age median of the 149 pregnant women was 25 years [21-31], pre-pregnancy BMI was 26.22 kg/m² [22.16-31.21] and gestational age was 16.2 weeks [13.05-18.10]. The best measurements correlated with VAT and TAT were MUAC and SBSF, both of which showed a higher correlation than pre-pregnancy BMI. CONCLUSIONS: It is possible to provide a practical and reliable estimate of VAT and TAT from the anthropometric evaluation (MUAC or SBSF) that is low cost, efficient and replicable in an outpatient clinic environment, especially in low- and middle-income countries.
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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) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".