Frailty, oral health and nutrition in geriatrics inpatients: A cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: Poor nutritional status is a risk factor for the development of frailty. Likewise, oral health is independently associated with nutrition. The potential association between oral health and frailty in hospitalised elderly adults has, however, not previously been investigated. OBJECTIVE: To investigate the relationship between oral health and frailty in hospitalised elderly adults and to identify the predictors of frailty. METHOD: A cross-sectional study of 168 geriatric inpatients >65 years old was performed from August to December 2016. Patients of non-English speaking background, with impaired cognition (MMSE <24), severe hearing or visual impairment or active delirium were excluded. Oral health, nutrition and frailty were assessed using previously validated tools, namely the Geriatric Oral Health Assessment Index (GOHAI), Mini Nutrition Assessment (MNA) and Reported Edmonton Frailty Scale (REFS). Other data collected included demographics, co-morbidities, level of education and smoking/alcohol history. RESULTS: On univariate analysis, the REFS score decreased with better nutritional status/higher MNA (P < 0.001) and better self-reported oral health/higher GOHAI (P = 0.023). Nutritional status accounted for 17% of variability in frailty assessment. On multivariate analysis, co-morbidities (P < 0.001), MNA (P < 0.001) and living in residential care (P < 0.001) were independent predictors of frailty. After adjusting for nutrition and co-morbidities, self-reported oral health was found to have an independent negative association with frailty (P = 0.019). CONCLUSION: Poor self-reported oral health was found to be independently associated with frailty. Further research should be directed at whether interventions to maintain good oral health can prevent or slow the progression of frailty.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".