Anthropometrical measurements and maternal visceral fat during first half of pregnancy: a cross-sectional survey
Bibliographic record
Abstract
Abstract Background Prenatal care is fundamental for achieving good results in the outcome of pregnancy, however, the coverage in Brazil is still low. In the impossibility of pre-gestational weight measure and subsequent body mass index (BMI) values, the others anthropometric measurements are useful and may be ideal for measuring the nutritional status of pregnant women, especially in low- and middle-income countries. The aim of this study was to assess the anthropometrical measurements during pregnancy and compared it to maternal ultrasound visceral adipose tissue.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 skin folds – TSF and subscapular skin folds – SBSF), and ultrasound variables (visceral adipose tissue – VAT and total adipose tissue – TAT) were collected. To verify the correlation of anthropometric and ultrasound measurements, non-adjusted and adjusted Spearman correlation was used. The study was approved by the ethics committees.Results Among the 149 pregnant women, 54.8% (n=80) were white race. The age median was 25 years [21 - 31], pre-pregnancy BMI was 26.22kg/m 2 [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, being anthropometric measurements with a higher correlation than pre-pregnancy BMI.Conclusion It is possible to provide a practical and reliable estimate of VAT and TAT from anthropometric evaluation (MUAC or SBSF) that is low cost, efficient and replicable in outpatient clinic environment, especially in low- and middle-income countries.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".