The relation between teeth loss and cognitive decline among Saudi population in the city of Riyadh: A pilot study
Bibliographic record
Abstract
Teeth are necessary for sensory input to the brain during the chewing process, but how the decrease in this sensory input, due to loss of teeth, may cause weak memory and lead to cognitive decline is not well understood. This pilot public survey aiming to assess the correlation between the number of missing teeth, periodontal disease, and cognitive skill in the city of Riyadh. A multicenter cross-sectional survey, targeting geriatric population aged ≥60 years, was performed in Riyadh City, Saudi Arabia. The Montreal Cognitive Assessment (MoCA) was conducted to all participants to assess their cognitive function. Assessment of oral health status was carried out, including the number of present dentation and their periodontal status. Community periodontal-index (CPI) was used to assess the periodontal condition. The primary variables were number of missing teeth, periodontal disease and MoCA test scores. Chi-square test and Pearson’s correlation coefficients were computed and the significant P- value was set at <0.05. Of 95 participants, overall, 57 (60%) and 38 (40%) were male and female, respectively, with a mean age of 65.67 ± 6.32 years. Females showed more significant cognitive decline than males (P < 0.001). Cognitive decline was significantly high in participants with low educational level 19 (95%), unemployment 41 (79%), and lower income people 26 (79%), while being cognitive intact was significantly higher in highly educated 13 (87%), retired 21 (62%), and higher income people 28 (74%) at (P < 0.001). An advanced age and greater number of missing teeth are associated with lower MoCA test scores. No statistical significant correlation with regard to periodontal disease and MoCA test scores. Based on the preliminary data, positive correlation was confirmed when the number of missing teeth and cognitive skill were assessed. Therefore, larger, multi-center regional surveys are needed to investigate further this relationship.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".