The association of red and processed meat with gestational diabetes mellitus: Results from 2 Canadian birth cohort studies
Bibliographic record
Abstract
OBJECTIVE: Red and processed meat is considered risk factors of gestational diabetes mellitus (GDM), but the evidence is inconclusive. We aimed to examine the association between red and processed meat intake and odds of GDM among South Asian and White European women living in Canada. METHODS: This is a cross-sectional analysis of pregnant women from two birth cohorts: SouTh Asian biRth cohorT (START; n = 976) and Family Atherosclerosis Monitoring In earLY life (FAMILY; n = 581). Dietary intake was assessed using a validated 169-item semi-quantitative food-frequency questionnaire (FFQ). Multivariate logistic regression models were used to examine the associations between gestational diabetes and: 1) total red and processed meat; 2) unprocessed red meat; 3) processed meat and GDM after adjustment for potential confounders. RESULTS: There were 241 GDM cases in START and 91 in FAMILY. The median total red and processed meat intake were 1.5 g/d (START) and 52.8 g/d (FAMILY). In START, the multivariable-adjusted odds ratio (OR) showed neither lower nor higher intakes of unprocessed red meat (p-trend = 0.68), processed meat (p-trend = 0.90), or total red and processed meat (p-trend = 0.44), were associated with increased odds of GDM, when compared with medium intake. Similar results were observed in FAMILY except for processed meat intake [OR = 0.94 (95% CI 0.47-1.91), for medium versus low and OR = 1.51 (95% CI 0.77-2.29) for medium versus high; p-trend = 0.18] after adjusting for additional dietary factors such as the diet quality score, total fiber, saturated fat and glycemic load. CONCLUSION: Medium compared with low or high red and processed meat intake is not associated with GDM in White Europeans and South Asians living in Canada.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".