Three Tumor Patients with Total Maxillectomy Rehabilitated with Implant‐Supported Frameworks and Maxillary Obturators: A Follow‐Up Report
Bibliographic record
Abstract
BACKGROUND: Few reports are available on treatment using implant-supported frameworks with maxillary obturators after total maxillectomy on tumor patients. PURPOSE: To describe, evaluate, and report the clinical and radiographic performance of implant-supported frameworks and maxillary obturators after maxillectomy during the first years of function. MATERIALS AND METHODS: Three patients with cancer in the maxillary region treated by total maxillectomy were rehabilitated. Seventeen dental and two craniofacial implants were installed, and the patients each received implant-supported, screw-retained, three-unit frameworks with a U-shaped bar and obturators retained by four magnetic attachments. Clinical and radiographic data were collected up to 7 years of follow-up. RESULTS: The frequency of complications was low. Two craniofacial implants and one dental implant were loose and removed at abutment connection. No implants were lost after framework connection, and the mean marginal bone loss was small. CONCLUSION: Within the limitations of this report, dental implants are useful for rehabilitation of total maxillectomy patients, and a three-unit, screw-retained, implant-supported framework with maxillary obturator retained by magnetic attachment is a successful treatment concept for this patient group.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".