Mastication in maxillectomy patients: A comparison between reconstructed maxillae and implant supported obturators: A cross‐sectional study
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to compare masticatory performance and patient reported eating ability of maxillectomy patients with implant-supported obturators and patients with surgically reconstructed maxillae. METHODS: This cross-sectional study was conducted at the University of Alberta, Edmonton, Canada and at Maastricht University Medical Centre (MUMC+), Maastricht, The Netherlands. Eleven surgically reconstructed maxillectomy patients have been included at University of Alberta and nine implant-supported obturator patients at MUMC+. The mixing ability test (MAT) was used to measure masticatory performance. In addition, the oral health related quality of life (OHRQoL) was measured with shortened versions of the oral health impact profile (OHIP) questionnaire. Values of the implant-supported obturator group versus the surgical reconstruction group were compared with independent t-tests in case of normal distribution, otherwise the Mann-Whitney U test was applied. RESULTS: Patients with reconstructed maxillae and patients with implant-supported obturator prostheses had similar mean mixing ability indices (18.20 ± 2.38 resp. 18.66 ± 1.37; P = .614). The seven OHRQoL questions also showed no differences in masticatory ability between the two groups. CONCLUSION: With caution, the results of this study seem to confirm earlier results that implant-supported obturation is a good alternative to surgical reconstruction for all Class II maxillary defects. With both techniques, the masticatory performance is sufficiently restored, with careful planning being highly desirable.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".