The potential causal effect of the pre-pregnancy dietary phytochemical index on gestational diabetes mellitus: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Phytochemicals are non-nutritive bioactive compounds with beneficial effects on the metabolism of glucose. This study aimed to clarify the possible causal effect of the pre-pregnancy dietary phytochemical index (DPI) on gestational diabetes mellitus (GDM). METHODS: In this prospective cohort study 1,856 pregnant women aged 18-45 years who were in their first trimester, were recruited and followed up until delivery. The dietary intakes of participants were examined using an interviewer-administered validated 168-item semi-quantitative food frequency questionnaire (FFQ). Inverse probability weighting (IPW) of propensity scores (PS), estimated from the generalized boosted model (GBM) were used to obtain a adjusted risk ratio (aRR) for potential confounders. RESULTS: During the follow-up period, 369 (19.88%) women were diagnosed with GDM. DPI scores ranged from 6.09 to 89.45. There was no association between DPI scores and GDM (aRR: 1.01, 95% confidence interval [CI]: 0.92, 1.08; p trend = 0.922). When comparing DPI quartile 4 (most pro-phytochemical content) to quartile 1 (few phytochemical contents), there was no significant difference between them (aRR: 0.97; 95% CI: 0.75, 1.25; p = 0.852). Also, there was no significant difference between DPI quartile 3 and quartile 1 (aRR: 1.04; 95% CI: 0.81, 1.34; p = 0.741) as well as DPI quartile 2 and quartile 1 (aRR: 0.92; 95% CI: 0.71, 1.21; p = 0.593). CONCLUSIONS: Although this data did not support the association between pre-pregnancy DPI scores and GDM, further cohort studies to ascertain the causal association between them are warranted.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".