Outcome of implants placed to retain craniofacial prostheses – A retrospective cohort study with a follow‐up of up to 30 years
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it