Periodontal disease, tooth loss and dementia: Is there a link? A systematic review
Bibliographic record
Abstract
BACKGROUND: It has been suggested that dementia is caused by neuronal damage due to chronic inflammation from peripheral sources such as the oral cavity in periodontal disease. OBJECTIVE: The aim of our review was to assess the risk of dementia or cognitive impairment associated with chronic periodontitis and multiple tooth loss. MATERIALS AND METHODS: An extensive search of electronic databases of articles on the relation between periodontitis, tooth loss and dementia published on or before April 2016 was conducted. Experimental and human studies that provided a description consistent with multiple tooth loss, chronic periodontal disease and cognitive impairment obtained by validated methods were selected. The data extracted from the articles included study design, country of origin, sample size, methods used to assess periodontitis and cognition, average age at the baseline and number of years of follow-up. The Newcastle-Ottawa scale was used to assess the quality of human studies. RESULTS: The literature search yielded 756 articles which were independently screened, and 16 articles were included in the review. Four human studies reported an association of subsequent dementia with multiple tooth loss. One human study reported that chronic periodontal disease was associated with dementia. Eight experimental studies demonstrated an association between cognitive impairment and tooth loss. CONCLUSION: The literature on chronic periodontitis and multiple tooth loss as risk factors to dementia remains inconclusive. More randomised clinical trials on the association between periodontitis and dementia with uniform criteria for evaluation and diagnosis of periodontitis are warranted.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".