Construction and validation of the quality of life measure for dentine hypersensitivity (DHEQ)
Bibliographic record
Abstract
AIM: To develop and validate a condition specific measure of oral health-related quality of life for dentine hypersensitivity (Dentine Hypersensitivity Experience Questionnaire, DHEQ). MATERIALS AND METHODS: Questionnaire construction used a multi-staged impact approach and an explicit theoretical model. Qualitative and quantitative development and validation included in-depth interviews, focus groups and cross-sectional questionnaire studies in a general population (n=160) and a clinical sample (n=108). RESULTS: An optimized DHEQ questionnaire containing 48 items has been developed to describe the pain, a scale to capture subjective impacts of dentine hypersensitivity, a global oral health rating and a scale to record effects on life overall. The impact scale had high values for internal reliability (nearly all item-total correlations >0.4 and Cronbach's α=0.86). Intra-class correlation coefficient for test-retest reliability was 0.92. The impact scale was strongly correlated to global oral health ratings and effects on life overall. These results were similar when DHEQ was validated in a clinical sample. CONCLUSIONS: DHEQ shows good psychometric properties in both a general population and clinical sample. Its use can further our understanding of the subjective impacts of dentine sensitivity.
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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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".