Hardness of the habitual diet and its relationship with cognitive function among 70‐year‐old Japanese elderly: Findings from the <scp>SONIC</scp> Study
Bibliographic record
Abstract
BACKGROUND: There is increasing evidence of causal links between poor mastication and cognitive impairment, but possible effects of dietary hardness, which clearly affects mastication, on cognitive function are unknown. OBJECTIVE: We investigated the hypothesis that hardness of the habitual diet would be associated with cognitive function among older Japanese adults. METHODS: The subjects of this cross-sectional study were 635 Japanese community-dwelling people aged 69-71 years. The masticatory muscle activity required for the habitual diet was used to determine dietary hardness. Consumption of 38 foods was assessed by a validated, brief-type, self-administered diet history questionnaire. A published database was then used to estimate the masticatory muscle activity involved in the ingestion of these foods. The Japanese version of the Montreal Cognitive Assessment (MoCA-J) was used for the measurement of cognitive function. RESULTS: The principal contributors to dietary hardness were cooked rice (28.0%), green leafy vegetables (5.1%), dried fish (4.9%), and pork and beef (4.6%). There was a positive association between dietary hardness and MoCA-J score that was robust to adjustment for potential confounders (MoCA-J score per 100-unit increase in dietary hardness: β = 0.83 [95% CI: 0.08, 1.59], P = 0.03). These results did not change materially even after exclusion of subjects who reported substantial changes in their diet for any reason (β = 0.94 [95% CI: 0.02, 1.86], P = 0.04). CONCLUSION: This preliminary cross-sectional study suggests that dietary hardness might have a beneficial effect on cognitive function in older Japanese people. Further prospective studies with more accurate measurements are needed to confirm this finding.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".