P28.05: First trimester measurement of subcutaneous and visceral adipose tissue thickness for the prediction of gestational diabetes
Bibliographic record
Abstract
To estimate the predictive value of first-trimester subcutaneous (SAT) and visceral (VAT) adipose tissues measurement for gestational diabetes. We performed a prospective study of pregnant women recruited at the time of 11–14 weeks ultrasound. Videos of abdominal wall above the uterus were collected and stored for subsequent measurement of SAT and VAT performed by 2 investigators blinded to patients characteristics and obstetrical outcomes. Participants were followed until delivery. Outcomes of interest included gestational diabetes (GDM) and GDM requiring insulin. Parametric, non-parametric, ROC curves analyses and multivariate linear regression analyses were used. 870 participants were recruited. We observed that 1st trimester VAT (Area under the ROC curve: 68%; 95%CI: 61 – 75%) and SAT (AUC: 65%; 95%CI: 57 – 72%) thicknesses were predictive of GDM. First-trimester VAT (AUC: 74%; 95%CI: 64 – 83%) and SAT (AUC: 68%; 95%CI: 58 – 77%) thicknesses were also predictive of GDM requiring insulin. However, only VAT thickness remains significant in multivariate regression analysis (p < 0.0001). We calculated that a VAT thickness >34 mm was associated with a significant risk of developing gestational diabetes (relative risk: 3.4; 95%CI: 2.0 – 5.6) and more particularly gestational diabetes requiring insulin (relative risk: 5.7; 95%CI: 2.8 – 11.5). First-trimester measurement of visceral adipose tissue with ultrasound is a strong predictor of GDM and particularly GDM requiring insulin.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".