Masticatory function in patients treated for oral cancer
Bibliographic record
Abstract
This thesis describes the effect of oral oncological intervention on masticatory performance. It represents an attempt to study many factors that either improve or deteriorate masticatory performance, based on both objective and self-reported outcomes. The objectives were to identify factors that result in either a better or worse masticatory performance in patients who have been treated for oral cancer and to determine whether digitally planned reconstructions lead to better masticatory performance. Chapter 2 focuses on the association of a mixing-ability test developed by the UMC Utrecht. The test, which uses a two-colored wax tablet and self-reported food-type chewing ability, was assessed in 123 patients who had been treated for oral cancer. The test involving 20 chewing strokes revealed that patients with MAI scores below 20 were likely to be able to chew all food types, with patients scoring below 24 being able to chew only soft food types and those with scores of 24 and higher being unlikely to be able to chew either soft or solid food types. Chapter 3 focuses on the course of chewing performance in 123 people confronted with cancer of oral tissues up to five years after treatment. We found that chewing performance after treatment was significantly worse than it had been before treatment. Chapter 4 provides further elaboration on factors that influence chewing performance, as derived from Chapter 3. Longitudinal results were presented with regard to several tongue functions (i.e., tongue sensory function, mobility and force). Significant deterioration was found for all functional aspects of the tongue except tongue force, which was apparently not affected by either treatment or recovery. No recovery was observed for tongue mobility, thermal, or tactile sensory function. The mixed-model procedure demonstrated that the maximum bite force, prosthetic state, time after surgery, number of occlusal units, tumor location, tongue force, tactile, and thermal sensory function significantly influenced chewing performance. The complete model was translated into a tool for calculating the mixing ability index (masticatory performance outcome), thereby allowing investigation of the impact of all relevant factors under different circumstances. The tool is not predictive, and it is intended only to illustrate the effects of relevant factors. It can be opened by following the URL: http://maicalc.azurewebsites.net/ Chapters 5 and 6 report on the masticatory performance, bite force and health-related quality of life outcomes of digitally planned reconstructions versus conventional techniques respectively upper and lower jaw (Alberta Reconstructive Technique). The procedure includes digital planning to create surgical guides for performing tumor surgery, reconstruction, and immediate implant placement in the fibula in a single procedure. In both chapters, the number of included patients is low, thus firm conclusions may not be drawn. However, it seems that the digitally planned reconstructed patients have better masticatory function and bit force than the conventional groups.
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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 | 0.000 | 0.001 |
| 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 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".