Associations Between Amniotic Fluid Minerals and Fetal Ultrasound Measurements
Bibliographic record
Abstract
Background Amniotic fluid (AF) contains macro‐ and micro‐minerals (MMM) ingested by the fetus through fetal swallowing, but few studies have examined their influence on fetal growth. Objective To determine the impact of 15 MMM concentrations (Mg, K, Ca, Al, Ni, Cu, Zn, Rb, Sr, Ag, Pb, Fe, Cr, As, Se) in 2 nd trimester AF on 5 fetal ultrasound measurements: estimated weight, bi‐parietal diameter, head circumference, abdominal circumference, and femur length in early (16‐20 wk) and late (32‐36 wk) gestation. Methods: AF was collected at amniocentesis (12‐20 wk). AF MMM concentrations were measured by ICP‐MS. Each fetal ultrasound measurement was tested as a dependent variable using multiple regressions, with each MMM entered as an independent variable while controlling for selected variables (multivitamin‐mineral supplementation, gestational age, estimated weight, amniocentesis wk, pre‐pregnancy BMI, parity, maternal height, ethnicity). Results: In early pregnancy, copper was associated with both lower bi‐parietal diameter and lower abdominal circumference; nickel, zinc and lead with lower head circumference; copper and zinc with greater abdominal circumference; and selenium with lower femur length. In late pregnancy, calcium was associated with greater bi‐parietal diameter and lead with greater abdominal circumference. In addition, calcium was associated with greater change in bi‐parietal diameter; nickel and copper with greater change in head circumference; and selenium with greater change in femur length from early to late pregnancy. Conclusion AF concentrations of calcium, copper, zinc, lead, nickel and selenium are associated with fetal growth changes during pregnancy independent of multivitamin‐mineral supplementation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".