Predictors of Satisfaction with Dentures in a Cohort of Individuals Wearing Old Dentures: Functional Quality or Patient‐Reported Measures?
Bibliographic record
Abstract
PURPOSE: To examine the extent to which denture satisfaction can be determined by a measure of the denture's functional quality and by patient-reported measures. MATERIALS AND METHODS: This study used data obtained from 117 edentulous individuals with a mean age of 73.7 (SD = 5.6) years in southern Brazil. The edentulous individuals rated their levels of general satisfaction with their actual dentures, using a visual analog scale. Explanatory variables included the individual's information about ability to chew, ability to speak, esthetics, and sociodemographic factors. The dentures were evaluated using the validated 9-item Functional Assessment of Dentures instrument. Bivariate statistical analyses and Poisson regression models (prevalence ratio [PR]; 95% CI; p < 0.05) were used to test the association of explanatory variables with patients' general satisfaction with their complete dentures. RESULTS: There was a statistically significant association between patients' general satisfaction and stability of maxillary (rocking movement) (adjusted PR = 1.28; 95% CI: 1.07-1.52) and mandibular dentures (occlusal displacement) (adjusted PR = 1.68; 95% CI: 1.16-2.43), masticatory ability (adjusted PR = 1.54; 95% CI: 1.08-2.19), and the age of the mandibular denture (adjusted PR = 1.47; 95% CI: 1.10-1.97). CONCLUSIONS: The results of this study indicated that measures of denture stability, masticatory ability, and age of dentures appeared to be determinants of patients' satisfaction with dentures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".