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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".