Supplementation with Docosahexaenoic Acid (DHA) in Women with Gestational Diabetes Mellitus (GDM) in Chile: Benefit to Glucose Tolerance and Plasma Lipids
Bibliographic record
Abstract
Dietary supplementation of healthy pregnant women with DHA, benefits both the mother and baby. Women with GDM have exaggerated plasma lipids that accompany their insulin resistance and their infants have DHA concentrations that are approximately one‐half those of infants born to women without GDM (Wijendran et al, 2000), GDM in Chile has an incidence that is documented at approximately 19% with screening in both the 2 nd and 3 rd trimesters. In this study we examined the effect of supplementation with 600 mg of DHA on parameters that are altered in GDM. Women with GDM (n=15) were supplemented with 600 mg/d of DHA (n=6) or placebo (n= 9) beginning at 24–30 wks of gestation; case histories were recorded and blood samples were collected for the determination of lipids and HbA1c. Blood collections were repeated at 36 wks. There were no differences between groups in plasma lipids or HbA1c at the beginning of the study. At 36 wks the placebo group had a significantly higher increase in total cholesterol, LDL‐cholesterol and HbA1c compared to the DHA group (24.3 mg/dl vs. 3.4 mg/dl, p<0.05; 16.6 mg/dl vs. 0, p<0.05; 0.2 mg/dl vs. −0.22 mg/dl, p<0.05, respectively). These results point to a benefit of DHA supplementation for the mother with GDM. (Supported in part by FONIS SA04I2054 (Chile) and Nestec, Ltd., Switzerland.)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".