Outcome of implants placed to retain craniofacial prostheses – A retrospective cohort study with a follow‐up of up to 30 years
Bibliographic record
Abstract
OBJECTIVES: To retrospectively assess the treatment outcomes of endosseous implants placed to retain craniofacial prostheses. MATERIAL AND METHODS: Patients with craniofacial defects resulting from congenital disease, trauma, or oncologic treatment had implant retained prostheses placed in the mastoid, orbital, or nasal region and then assessed over a period of up to 30 years. Implant survival rates were calculated with the Kaplan-Meier method. Clinical assessments consisted of scoring skin reactions under the prosthesis and the peri-implant skin reactions. Possible risk factors for implant loss were identified. Patient satisfaction was evaluated using a 10-point VAS-scale. RESULTS: A total of 525 implants placed in 201 patients were included. The median follow up was 71 months (IQR 28-174 months). Implants placed in the mastoid and nasal region showed the highest overall implant survival rates (10-year implant survival rates of 93.7% and 92.5%, respectively), while the orbital implants had the lowest overall survival rate (84.2%). Radiotherapy was a significant risk factor for implant loss (HR 3.14, p < 0.001). No differences in implant loss were found between pre- and post-operative radiotherapy (p = 0.89). Soft tissue problems were not frequently encountered, and the patients were highly satisfied with their implant-retained prosthesis. CONCLUSION: Implants used to retain craniofacial prostheses have high survival and patient satisfaction rates and can thus be considered as a predictable treatment option. Radiation is the most important risk factor for implant loss.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".