Masticatory function and mortality among older adults living in long‐term care facilities in Brazil
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between mortality and masticatory function in older adults living in long-term care facilities (LTCFs), controlling for demographic and health covariates. BACKGROUND: Poor oral health has been associated with mortality; however, no previous study investigated whether objective and self-reported poor masticatory function is a predictor of early mortality in LTCFs. MATERIALS AND METHODS: Baseline characteristics of 295 participants were collected, including age, sex, polypharmacy, mobility, activities of daily living, frailty, nutritional status, and objective (masticatory performance - chewing gum) and self-reported masticatory function. The participants were followed-up with for 4 years to record the mortality data. Cox regression models were run to analyse the data (α = .05). RESULTS: During the 4-year follow-up, 124 (42.0%) participants died. Older adults with poor masticatory performance (hazard ratio [HR] = 1.59, 95% confidence interval [95% CI] = 1.07-2.36) and those who self-reported masticatory dysfunction (HR = 1.48, 95% CI = 1.01-2.16) were at higher risk of early death than those with good mastication. However, in a multivariate model including both objective and self-reported masticatory function, only the objective measurement remained associated with early death (HR = 1.52, 95% CI = 1.02-2.27). CONCLUSION: Poor masticatory performance seems to be associated with early death in older adults living in LTCFs, but they may have shared risk factors accumulated throughout life that were not covered by the study period.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".