Malignant Tumors of the Maxilla: Virtual Planning and Real‐Time Rehabilitation with Custom‐Made R‐zygoma Fixtures and Carbon–Graphite Fiber‐Reinforced Polymer Prosthesis
Bibliographic record
Abstract
BACKGROUND: Oral cancer is a mutilating disease. Because of the expanding application of computer technology in medicine, new methods are constantly evolving. This project leads into a new technology in maxillofacial reconstructive therapy using a redesigned zygoma fixture. PURPOSE: Previous development experiences showed that the procedure was time-consuming and painful for the patients. Frequent episodes of sedation or general anesthetics were required and the rehabilitation is costly. The aim of our new treatment goal was to allow the patients to wake up after tumor surgery with a functional rehabilitation in place. MATERIALS AND METHODS: Stereolithographic models were introduced to produce a model from the three-dimensional computed tomography (CT). A guide with the proposed resection was fabricated, and the real-time maxillectomy was performed. From the postoperative CT, a second stereolithographic model was manufactured and in addition, a stent for the optimal position of the implants. Customized zygoma implants were installed (R-zygoma, Integration AB, Göteborg, Sweden). A fixed construction was fabricated by using a new material based on poly(methylacrylate) reinforced with carbon/graphite fibers and attached to the implants. On the same master cast, a separate obturator was fabricated in permanent soft silicon. RESULTS: The result of this project showed that it was possible to create a virtual plan preoperatively to apply during surgery in order for the patient to wake up functionally rehabilitated. CONCLUSION: From a quality-of-life perspective, it is an advantage to be rehabilitated fast. By using new computer technology, pain and discomfort are less and the total rehabilitation is faster, which in turn reduces days in hospital and thereby total costs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".