Survival of Dental Implants in Irradiated Head and Neck Cancer Patients: A Retrospective Analysis
Bibliographic record
Abstract
PURPOSE: To study the long-term survival of dental implants placed in irradiated bone in subjects who received radiation for head and neck cancer. MATERIALS AND METHOD: A retrospective chart review was conducted for all patients who received dental implants following radiation treatment for head and neck cancer between May 1, 1987 through July 1, 2008. Only patients irradiated with a radiation dose of 50 Gy or greater and those who received dental implants in the irradiated field after head and neck radiation were included in the study. The associations between implant survival and patient/implant characteristics were estimated by fitting univariate marginal Cox proportional hazards models. RESULTS: A total of 48 patients who had prior head and neck radiation had 271 dental implants placed during May 1987 to July 2008. The estimated survival at 1, 5, and 10 years was 98.9%, 89.9%, and 72.3%, respectively. Implants placed in the maxilla were more likely to fail than implants placed in the mandible (p = .002).There was also a tendency for implants placed in the posterior region to fail compared with those placed in the anterior region (p =.051). CONCLUSION: Dental implants placed in irradiated bone have a greater risk for failure. Survival is significantly influenced by the location of the implant (maxilla or mandible, anterior or posterior).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".