Tooth Loss in Periodontitis Patients—A Risk Factor for Mild Cognitive Impairment: A Systematic Review and Meta—Analysis
Bibliographic record
Abstract
BACKGROUND: Periodontal disease and tooth loss have been long suggested as risk factors of mild cognitive impairment. The underlying mechanisms could be systemic chronic inflammatory mediators, direct pathologic challenge to the nervous system, malnutrition and/or loss of neurosensory stimulation input causing brain atrophy. This review aimed to examine the existing literature studies linking the effect of periodontal disease and tooth loss on the development of mild cognitive impairment. METHODS: A systematic review using PEO was conducted. Three electronic databases, namely Embase, Medline and DOSS (UCLan), were searched for relevant articles published up to April 2023. Google Scholar and a hand search were also conducted to ensure no relevant studies had been missed. The Newcastle-Ottawa scale was used to assess the quality of studies. RESULTS: The findings showed that chronic periodontitis and tooth loss, both individually and in combination, led to an increased risk of mild cognitive decline in adults over 50 years. Within the limitations of this review, periodontitis and tooth loss both contribute to an increased risk of mild cognitive impairment and dementia, but the evidence so far is not strong. CONCLUSIONS: In future, more robustly designed studies investigating periodontal disease and tooth losslink with cognitive health decline are required with a longer follow-up duration.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".