Fetal overgrowth (FOG) is associated with early perturbations in amniotic fluid (AF) glucose, insulin and insulin like growth factors (IGF) in mothers with and without gestational diabetes (GDM)
Bibliographic record
Abstract
FOG occurs in mothers with and without GDM. Few biochemical markers have been identified. We investigated the possibility that perturbations in 2 nd trimester AF glucose, insulin, IGF 2 or its binding proteins (BP) 1 or 3 might underscore FOG and that these early AF disturbances might differ in mothers with or without GDM. Our study population (n=701) was stratified as follows: mothers were classified as GDM or NON GDM and their offspring as appropriate (AGA) or large for gestational age (LGA). ANCOVA, multiple linear and logistic regressions were performed and, using pairs of AF constituents, 2D Bayesian probability plots were created to predict LGA. In the GDM population AF insulin was positively and IGF 2 was negatively associated with growth centile, while elevated AF glucose increased the odds of neonates being born LGA. In the NON GDM population AF insulin was positively and BP 3 negatively associated with growth centile, but the likelihood of LGA was not increased by AF glucose. Bayesian probability plots showed that specific AF constituents separated LGA from AGA infants in GDM mothers but were less discriminating in NON GDM mothers. In conclusion, fetal exposure to increased AF glucose in the first trimester increases the risk of delivery of an LGA infant in GDM mothers. Lower BP 3 in NON GDM mothers and IGF 2 in GDM mothers provide further evidence that perturbations in growth factors may underscore FOG. [Funded by CIHR] Grant Funding Source : CIHR
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 | 0.001 | 0.004 |
| 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 | 0.000 | 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".