Validation of the Brazilian versions of two inventories for measuring oral health‐related quality of life of edentulous subjects
Bibliographic record
Abstract
OBJECTIVES: To analyse the validity of the Brazilian versions of OHIP-EDENT and GOHAI as assessment tools of edentulous subjects' OHRQoL. BACKGROUND: Inventories for measuring oral health-related quality of life (OHRQoL) are important in clinical studies regarding oral rehabilitation. However, there is a need for comprehensive validation after translation into different cultural settings. MATERIALS AND METHODS: The sample comprised of 100 complete denture wearers (29 men, 71 women, mean age of 65.2 ± 9.9 years). The associations between each OHRQoL inventory and other variables served as measurements of construct validity. Data analysis comprised the Spearman correlation test as well as multiple regression using the OHRQoL inventories as dependent variables and the other scales as determinants. RESULTS: Both OHRQoL inventories showed good correlation with denture satisfaction, whereas lower correlation coefficients were found among the inventories and the HAD subscales. Denture satisfaction alone explained 48% and 39% of the variance found for the OHIP-EDENT and GOHAI, respectively, as assessed by multiple regression. A smaller effect was found for OHIP-EDENT. CONCLUSION: Both OHIP-EDENT and GOHAI showed good construct validity for measurement of OHRQoL of edentulous subjects.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".