Processed foods in the context of a vegan diet, and changes in body weight and severe hot flashes in postmenopausal women: a secondary analysis of a randomized clinical trial
Bibliographic record
Abstract
OBJECTIVES: A plant-based diet has been shown to reduce hot flashes, partly by weight loss. Because some plant-based foods are highly processed, this secondary analysis assessed associations between consumption of processed foods, body weight, and severe hot flashes in postmenopausal women. METHODS: Participants (N=84) were randomly assigned to a low-fat vegan diet supplemented with soybeans (n=42) or an omnivorous control group (n=42) for 12 weeks. Three-day diet records were analyzed using the Nutrition Data System for Research software; the NOVA classification was used to assess processed food consumption. A repeated measures ANOVA was used for statistical analyses. RESULTS: Consumption of unprocessed or minimally processed animal foods decreased in the vegan group, compared with the control group; effect size: -106 g/d (95% CI: -169 to -42); P=0.002. The reduction in consumption of unprocessed or minimally processed animal foods was associated with weight loss (r=+0.45; P<0.001) and a reduction in severe hot flashes (r=+0.31; P=0.01). Similarly, consumption of ultra-processed animal foods decreased in the vegan group by 60 g/d (95% CI: -105 to -15); P=0.004, with no significant change in the control group; effect size: -65 g/d (95% CI: -115 to -16); P=0.01. The reduction in consumption of ultra-processed animal foods was associated with weight loss (r=+0.43; P<0.001). Consumption of ultra-processed plant-based foods changed only minimally and nonsignificantly and was not associated with changes in weight or hot flash frequency. CONCLUSIONS: These findings suggest that, in the context of a soybean-supplemented vegan diet, replacing the consumption of both unprocessed or minimally processed and ultra-processed animal foods with plant foods (regardless of the level of processing), was associated with significant weight loss and a reduction in severe hot flashes.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".