Higher weekly white rice consumption is associated with an increased risk of incident MCI : a two-year follow-up study of elderly people in Shanghai Community
Bibliographic record
Abstract
Abstract Background: There have been no longitudinal studies of white rice consumption and cognitive impairmentMethods: This was a 2-year longitudinal follow-up study. Data were obtained from the cohort study on the brain health of the elderly in Shanghai. There were 620 (224 men and 396 women) subjects aged ≥ 60 years. Weekly white rice consumption was assessed using a quantitative food frequency questionnaire. Cognitive function was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) and the diagnosis of mild cognitive impairment (MCI) was based on the revised Petersen’s diagnostic algorithm. The association between weekly white rice consumption and cognitive function was investigated by Cox regression analysis and the ROC curve. Results: During a mean follow-up period of 2 years, 471 individuals without baseline cognitive impairment developed incident mild cognitive impairment. In the overall cohort, higher weekly white rice consumption was associated with an increased risk of MCI (p=0.019, HR=1.051, 95%CI:1.008~1.096) and was independent of age, education and drinking. The ROC curve indicated that weekly white rice consumption had a mild-to-moderate effect in predicting MCI (below the curve was 0.591, p=0.07, 95%CI:0.527~0.655). Conclusions: higher weekly white rice consumption is associated with an increased risk of incident MCI
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".