Relationship between rice consumption and body weight gain in Japanese workers: white versus brown rice/multigrain rice
Bibliographic record
Abstract
Increasing obesity rates have driven research into dietary support for body weight control, but previous studies have only assessed changes in body weight of ±3 kg. We investigated the relationships between white or brown/multi-grain rice consumption and 1-year body weight gain ≥3 kg in Japanese factory workers (n = 437). Routine medical check-up data from a 1-year nutrition and lifestyle cohort study were analysed. Participants were divided into white rice and brown/multi-grain rice consumption groups and further classified by tertile of rice consumption. Multiple logistic regression analyses were performed by tertile. At 1 year, high white rice consumption was significantly associated with increased risk of body weight gain ≥3 kg compared with low white rice consumption, maintained after adjustment for age, sex, and consumption of other obesogenic foods (p = 0.034). In the brown/multi-grain rice consumption group, however, there was no significant difference in risk between high and low consumption, even after multi-variate adjustment (p = 0.387). The consumption of white rice, but not brown rice/multi-grain rice, was positively correlated with the risk of a 1-year body weight gain of 3 kg or more. This suggests that brown rice/multi-grain rice consumption is useful for body weight control among Japanese workers.
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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 | 0.000 |
| 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".