Vitamin D and Gestational Diabetes Mellitus: Is There a Link?
Bibliographic record
Abstract
Gestational diabetes mellitus (GDM) is a very common condition among pregnant women worldwide with important metabolic implications on the mother and the offspring. Vitamin D status has been suggested to potentially play a role in GDM risk with no documented consequences for the offspring. The purpose of this article was to review currently available evidence on the relationship between vitamin D and GDM. Current evidence shows an association between vitamin D blood levels and risk of GDM, despite heterogeneity of results across studies limit the conclusions. Moreover, data from randomized controlled trials is scarce and resulting in null findings. Among the limitations to be noted, improving the standardization of dosages, the characteristics of individuals in the sample, and the appropriate outcome measurement could provide a more effective approach in understanding the relationship between vitamin D and GDM. In conclusions, despite observational studies may suggest that adequate vitamin D levels may decrease the risk of GDM compared to deficiency status, evidence from clinical trials is inadequate to draft any definitive conclusion regarding its supplementation. Future better designed randomized clinical trials taking into account a more integrated approach could provide clearer and definitive data on the outcomes of such a multifactorial condition.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".