The number of remaining teeth as a risk indicator of cognitive impairment: A cross‐sectional clinical study in <scp>Sado Island</scp>
Bibliographic record
Abstract
Most studies that have demonstrated an association between number of remaining teeth and cognitive impairment have treated teeth as a continuous variable, although the relationship is nonlinear. The aim of this cross-sectional study was to determine the critical number of remaining teeth in hospital outpatients at which the association with cognitive impairment becomes apparent. Japanese adults living on Sado Island who visited Sado General Hospital were invited to participate in Project in Sado for Total Health. In total, 2,530 adults were interviewed and had their teeth counted; 1,476 of these individuals also completed the Mini-Mental State Examination (MMSE) and underwent measurement of their serum high-sensitivity C-reactive protein (hsCRP) levels. Patients on dialysis and those with hsCRP ≥ 10 mg/L were excluded. The final study group consisted of 565 adults (290 men and 275 women) of mean age 69.8 (range 29-91) years. An MMSE score < 24 was considered to indicate cognitive impairment. The subjects were categorized according to whether they had an edentulous jaw or one to 10, 11-20, 21-27, or ≥28 remaining teeth. One hundred twenty-eight of the 565 study participants were diagnosed to have cognitive impairment. Multiple logistic regression analysis revealed associations of cognitive impairment with older age, ischemic heart disease, smoking, and alcohol consumption. After adjustment for covariates, having one to 10 remaining teeth was significantly associated with cognitive impairment. There is a significant association between having only one to 10 remaining teeth and cognitive impairment in hospital outpatients.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".