Predictors of oral health quality of life in older adults
Bibliographic record
Abstract
There is limited information regarding oral health status and other predictors of oral health-related quality of life. An association between oral health status and perceived oral health-related quality of life (OHQOL) might help clinicians motivate patients to prevent oral diseases and improve the outcome of some dental public health programs. This study evaluated the relationship between older persons' OHQOL and their functional dentition, caries, periodontal status, chronic diseases, and some demographic characteristics. A group of 733 low-income elders (mean age 72.7 [SD = 4.71, 55.6% women, 55.1% members of ethnic minority groups in the U.S. and Canada) enrolled in the TEETH clinical trial were interviewed and examined as part of their fifth annual visit for the trial. OHQOL was measured by the Geriatric Oral Health Assessment Index (GOHAI); oral health and occlusal status by clinical exams and the Eichner Index; and demographics via interviews. Elders who completed the four-year assessment had an average of 21.5 teeth (SD = 6.9), with 8.5 occluding pairs (SD = 4.6), and 32% with occlusal contacts in all four occluding zones. Stepwise multiple regressions were conducted to predict total GOHAI and its subscores (Physical, Social, and Worry). Functional dentition was a less significant predictor than ethnicity and being foreign-born. These variables, together with gender, years since immigrating, number of carious roots, and periodontal status, could predict 32% of the variance in total GOHAI, 24% in Physical, 27% in Social, and 21% in the Worry subscales. These findings suggest that functional dentition and caries influence older adults' OHQOL, but that ethnicity and immigrant status play a larger role.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".