Iron Supplementation in Non-Anemic Pregnancy and Risk of Developing Gestational Diabetes Mellitus
Bibliographic record
Abstract
Background: Routine iron supplementation for all pregnant women in order to reduce adverse neonatal outcomes has been a standard practice in developing countries, including Thailand. However, there is evidence that excess iron can affect glucose metabolism and may increase risk of gestational diabetes mellitus (GDM). This study aims to investigate the association of iron supplementation starting from early gestation and the risk of GDM in non-anemic women. Methods: This retrospective cohort study included non-anemic pregnant Thai women who received their first antenatal care and delivered at Vajira Hospital (Bangkok, Thailand) during January 2008 to December 2009. All pregnant women underwent oral glucose tolerance test during gestational weeks 24 - 28. The proportions of ongoing GDM and birth outcomes were compared between the early oral iron supplementation group (before 16 completed weeks) and the control group (after 16 weeks). Results: There were 1,935 non-anemic pregnant women, 966 in the early supplement group and 969 in the control group. The early supplement group had significantly higher prevalence of GDM than the control group did (9.7% vs. 5.6%, RR: 1.83; 95% CI: 1.29 - 2.59). No significant differences in maternal anemia, gestational age at birth, or neonatal birth weight were observed between groups. Conclusions: Early antenatal iron supplementation in non-anemic pregnant women was found to be associated with significantly increased risk of GDM. J Endocrinol Metab. 2018;8(6):139-143 doi: https://doi.org/10.14740/jem543
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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 | 0.002 |
| 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".