Maternal visceral adipose tissue during the first half of pregnancy predicts gestational diabetes at the time of delivery – a cohort study
Bibliographic record
Abstract
BACKGROUND: Gestational diabetes mellitus (GDM) is a common condition, often associated with high maternal and fetal morbidity. The use of new tools for early GDM screening can contribute to metabolic control to reduce maternal and fetal risk. This study aimed to ascertain whether maternal visceral adipose tissue (VAT) measurement by ultrasound during the first half of pregnancy can predict the occurrence of GDM during the third trimester. METHODS: A prospective cohort study of 133 pregnant women with gestational age ≤20 weeks in an outpatient setting. VAT depth was measured by ultrasound at the maternal periumbilical region. GDM status was obtained through hospital charts during hospitalization to delivery. A Receiver Operator Characteristic (ROC) curve was used to determine the optimum threshold to predict GDM. RESULTS: According to the ROC curve, a 45mm threshold was identified as the best cut-off value, with 66% of accuracy to predict GDM. Crude and adjusted odds ratios (OR) for GDM were 13.4 (95%CI 2.9-61.1) and 8.9 (95%CI 1.9-42.2), respectively. A similar result was obtained among pre-gravid non-obese women, with crude and adjusted OR of 16.6 (95%CI 1.9-142.6) and 14.4 (95%CI 1.7-125.7), respectively. Among pre-gravid obese patients, a 45mm threshold did not reach statistical significance to predict GDM. CONCLUSION: The high and significant OR found before and after adjustments provides additional evidence of a strong association between VAT and GDM. It appears that VAT measurement during the first half of pregnancy has great potential in identifying non-obese women at high risk for GDM. This evidence can assist obstetricians in correctly allocating resources among populations of pregnant women at risk, determined not only by pre-gravid body mass index (BMI).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".