Maternal adipose tissue to early preeclampsia risk detection: Is the time to maternal ultrasound beyond fetal evaluation?
Bibliographic record
Abstract
Abstract Introduction This study aims to determine the predictive capacity of isolated maternal periumbilical and epigastric fat measurements during pregnancy to hypertensive outcomes. Methods A cohort study was conducted with pregnant women in any trimester and followed until delivery to identify the outcomes of interest, preeclampsia (PE) and gestational hypertension (GH). The predictive capacity of fourth quartile measurements was compared with the first three quartiles of maternal subcutaneous and visceral adipose tissue from the periumbilical site (periumbilical m‐SAT and m‐VAT) (n = 155) and maternal adipose tissue from the epigastric site (preperitoneal m‐SAT and m‐VAT) (n = 261). The predictive ability of prepregnant body mass index (BMI) above 30 kg/m2 for PE and GH was also assessed. Results Fourth quartiles for the periumbilical ultrasound measurements were m‐VAT 52.7 mm and m‐SAT 21.7 mm. Preperitoneal site presents fourth quartiles m‐VAT 15.2 mm and m‐SAT 18.6 mm. Both m‐VAT and m‐SAT maternal periumbilical and preperitoneal sites are unable to predict PE, with the utmost sensitivity attributed to the periumbilical site m‐SAT at 54%. The best PE predictor odds ratio (OR) found was the prepregnant BMI consistent with obesity, with an OR of 3.2 (95% CI 1.1–9.4), whereas the best OR to GH predictor was preperitoneal m‐SAT with 8.9 (95% CI 2.3–34.6). Conclusion PE pathogenic mechanisms related to maternal abdominal adipose tissue include differences in molecular, cytological, and tissue levels not detected by ultrasound in a quantified gray scale assessment. Periumbilical or epigastric m‐VAT use is not able to predict PE during pregnancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.028 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".