Association of a plant‐based dietary pattern in relation to gestational diabetes mellitus
Bibliographic record
Abstract
AIM: The prevalence of gestational diabetes mellitus (GDM), which has adverse effects on mothers and their offspring, is increasing worldwide. The role of a plant-based dietary pattern as a determinant of GDM is not well understood. Therefore, we examined the association between plant-based dietary patterns and the risk of GDM. METHODS: We enrolled 460 pregnant women in this case-control study, of them 200 were cases and 260 were controls. Dietary intake of participants was evaluated using three 24-hour dietary records. Adherence to the plant-based dietary patterns was scored using three indices of the overall plant-based dietary index (PDI), healthy plant-based diet (hPDI) and unhealthy plant-based diet index (uPDI). The risk of GDM was compared across tertiles of PDI, hPDI and uPDI. RESULTS: After multivariable adjustment, we demonstrated that the high PDI score was inversely associated with risk of GDM (OR = 0.47; 95% CI: 0.28-0.78, P = 0.004), but there was no significant association between hPDI (OR = 1.03; 95% CI: 0.64-1.65, P = 0.884) or uPDI (OR = 1.65; 95% CI: 0.98-2.78, P = 0.06) and GDM risk. CONCLUSIONS: We found that following an overall plant-based diet was associated with lower risk of GDM. Future studies are warranted with longitudinal designs to confirm these findings.
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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 | 0.001 |
| 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".