The association between the number of teeth and frailty among older adults: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Tooth loss is common among the elderly and often correlates with aging. Existing studies on the link between tooth loss and frailty in older adults yield inconsistent results. This systematic review and meta-analysis aims to clarify the relationship. METHODS: A comprehensive search of PubMed, Web of Science, Embase, and Cochrane Library was conducted to find observational studies on tooth count and frailty in older adults. Study quality was assessed using the Newcastle-Ottawa scale. Heterogeneity was evaluated using Cochran's Q and I² statistics, and subgroup analyses identified factors influencing outcomes. Publication bias and sensitivity analysis confirmed result stability. RESULTS: From 1,903 articles, 22 comprising 25 studies with 36,406 participants were included. The meta-analysis showed a pooled odds ratio (OR) of 0.98 (95% CI: 0.97 - 0.99) for tooth count and frailty. Individuals with 20 or fewer teeth had a higher risk of frailty (pooled OR = 1.99, 95% CI: 1.57 - 2.53). The highest frailty risk was observed in Japan (pooled OR = 3.02), followed by China (2.27), the UK and USA (1.90), and other regions (1.25). Subgroup analyses revealed no significant differences by country, study design, setting, adjustment model, or frailty assessment tool (P > 0.05). CONCLUSIONS: There is a significant association between tooth count and frailty, particularly in those with 20 or fewer teeth. Policymakers should prioritize oral health within aging populations by promoting early preventive care and education to mitigate frailty risk. Robust, large-scale studies are needed to guide evidence-based interventions and public health policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".