Patient evaluation of treatment success as related to denture tooth type.
Bibliographic record
Abstract
PURPOSE: Tooth selection is considered an important factor in the construction of complete dentures that function harmoniously and comfortably and preserve the denture-supporting tissues. To bring a scientific background to clinical impressions, the hypothesis that different cusp angles bring different levels of patient satisfaction was tested. MATERIALS AND METHODS: Four different occlusal schemes were compared. The dependent variables (3 groups of visual analogue scores of patient satisfaction) were analyzed by means of multivariate analysis of variance (MANOVA) for repeated measures. RESULTS: All MANOVAs showed nonsignificant results for the effect of tooth type on the 3 groups of variables (P values between 0.1 and 0.8). CONCLUSION: The results did not show statistically significant differences in patient satisfaction among the different occlusal schemes. It is recommended that future research use more sensitive instruments to evaluate this specific aspect of treatment success.
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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 | 0.000 |
| 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) | 0.001 | 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 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".