Association between oral health and frailty: A systematic review of longitudinal studies
Bibliographic record
Abstract
OBJECTIVE: To systematically review longitudinal studies on the association between oral health and frailty indicated by any validated scale or index. BACKGROUND: Frailty and poor oral health are common among ageing populations; however, evidence from longitudinal studies is scarce. METHODS: Three databases (MEDLINE, EMBASE and LILACS) were searched for published literature up to July 2018 using prespecified search strategy. Grey literature was searched using OpenGrey and Google Scholar. Quality of included studies was checked using the Newcastle-Ottawa Quality Assessment Scale (NOS) for longitudinal studies. RESULTS: Five longitudinal studies from three countries (Mexico, Japan, and UK) that examined the association between oral health and frailty were identified. All studies used Fried's frailty phenotype criteria for measuring frailty. Oral health indicators were number of teeth, periodontal disease, oral functions (functional dentition with occluding pairs and maximum bite force), use of removable dentures, accumulation of oral health problems and dry mouth symptoms. The studies showed significant association of number of teeth (two studies), oral functions (two studies), accumulation of oral health problems and number of dry mouth symptoms with frailty incidence, whereas periodontal disease showed inconsistent associations. CONCLUSION: This systematic review identified significant longitudinal associations between oral health indicators and frailty that highlight the importance of oral health as a predictor of frailty in older age. There is a need for further research exploring the role of nutrition as a mediator of the relationship between oral health and frailty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".