Tooth Loss Increases the Risk of Diminished Cognitive Function
Bibliographic record
Abstract
Emerging evidence suggests that oral health is associated with cognitive function. This review aims to systematically assess this association in adult populations via prospective cohort study designs. Eligible study reports were identified by searching the MEDLINE (via Ovoid), EMBASE, PsycoINFO, and Cochrane Library databases. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated with a random effects model. From 1,251 identified articles, 10 were included in the systematic review and 8 in the meta-analysis. Random effects analysis showed, with statistically low heterogeneity, that individuals with suboptimal dentition (<20 teeth) were at a 20% higher risk for developing cognitive decline (HR = 1.26, 95% CI = 1.14 to 1.40) and dementia (HR = 1.22, 95% CI = 1.04 to 1.43) than those with optimal dentition (≥20 teeth). Studies on the association between periodontal disease and cognitive status showed conflicting results. Within the limits of the quality of published evidence, this meta-analysis lends further support to the hypothesis that tooth loss is associated with an increased risk of cognitive impairment and dementia. Knowledge Transfer Statement: Based on the published literature, the results of this study show that the risk for cognitive impairment and dementia increases with loss of teeth. This information adds to the evidence showing links between oral and general health and suggests that oral health strategies aimed to preserve teeth may be important in reducing risk of systemic disease.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".