The McGill Denture Satisfaction Questionnaire revisited: Exploratory factor analysis of a binational sample
Bibliographic record
Abstract
OBJECTIVES: To examine the McGill Denture Satisfaction Questionnaire (MDSQ) in terms of dimensionality, item reduction and construct validity in a binational sample of complete denture wearers. MATERIALS AND METHODS: We conducted secondary analyses of baseline data from two studies on implant-retained overdentures: a quasi-experimental study in the United States (n = 145) and a randomised trial in Brazil (n = 120). All participants wore upper/lower dentures and responded at baseline to the MDSQ items concerning their original mandibular dentures. A putative model of the MDSQ items resulted in two question subsets: (a) overall satisfaction, retention/stability, aesthetics, cleaning, speech and comfort, plus general chewing ability; (b) mastication of specific foods. Analyses focused on the internal consistency of each subset and possible item reduction, using Cronbach's alpha (Cα), inter-item correlation and exploratory factor analysis (EFA). RESULTS: The 1st subset showed high inter-item correlation for most question combinations and no redundancy (r ≤ .8). An item on cleaning had low correlation, but its removal does not increase internal consistency (Cα ≥ .83). Results were similar for both studies, with EFA showing a single significant factor (namely "overall satisfaction, lower denture") able to explain nearly 54% of the variance. The 2nd subset also shows strong internal consistency (Cα ≥ .95) and inter-item correlation, with a single factor representing 65% of the variation. CONCLUSIONS: This study discloses the reliability and construct validity of the MDSQ for patient-centred evaluation of complete dental prostheses in the edentulous mandible. Findings also support the use of both "overall satisfaction" and "masticatory ability" as summary scores, for improved outcome assessment.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".