Patient ratings of chewing ability from a randomised crossover trial: lingualised vs. first premolar/canine‐guided occlusion for complete dentures
Bibliographic record
Abstract
BACKGROUND: Complex procedures involving a facebow transfer and the use of lingualised teeth are deemed to have a positive influence on the chewing ability with complete dentures. OBJECTIVES: To determine if patients' ratings of their ability to chew depend on the method of complete denture fabrication. METHODS: Edentulous patients (n = 20) participated in a within-subject crossover trial. Each patient received two sets of new complete dentures. One pair was manufactured based on intraoral tracing of centric relation and facebow transfer; semi-anatomical teeth with lingualised occlusion denture (LOD) were chosen. The second pair was made using a simplified procedure without facebow transfer; jaw relations were recorded with wax occlusion rims, and anatomical teeth with a first premolar/canine-guidance (CGD) were selected. The dentures were delivered in randomised order, and each was worn for 3 months. Three months after delivery, patients' ratings of each new prosthesis were recorded on visual analogue scales for their ability to chew seven index foods. Repeated measurements analysis of variance was performed to investigate possible carry-over effects accounting for confounding by treatment period. RESULTS: When comparing the two treatments, participants rated their ability to chew in general, to masticate carrots, hard sausage, steak and raw apple in particular, was significantly better with the CGD (anatomical teeth) than with the LOD (p < 0.05). CONCLUSION: Comprehensive methods for the fabrication of complete dentures including semi-anatomical lingualised teeth and a full registration do not seem to influence the perceived chewing ability, when compared with more simple procedures. Chewing ability for tough foods appears to benefit from the use of anatomical teeth.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".