Outcomes of implant-based oral rehabilitation in head and neck oncology patients—a retrospective evaluation of a large, single regional service cohort
Bibliographic record
Abstract
BACKGROUND: The study reports on implant survival outcomes in head and neck cancer patients who received implant-based oral rehabilitation in a regional service centre. METHODS: A retrospective analysis of implant survival outcomes in patients treated in a regional service from 2012 to 2017 was performed. The primary outcome measure was implant survival. The secondary outcome measure was to assess the effect of covariates associated with implant failure including bone type, radiotherapy, chemotherapy, gender and surgical implant complications. Kaplan-Meier survival curves were applied to compare differences in the survival rates of groups of variables. Cox proportional hazards models were applied to identify covariates associated with implant failure. p value was set at 0.05. RESULTS: The sample was composed of 167 head and neck cancer patients who had 779 dental implants placed. Implant survival estimates were calculated: 3 years, 95.7% [95%CI 94.3-97.2%] and 5 years, 95.5% [95%CI 93.9-97.0%], with a median follow-up of 38 months. Gender (p = 0.09), radiotherapy (p = 0.16) and chemotherapy (p = 0.17) did not significantly influence implant survival, whereas implant failure was higher in transported (reconstructed) bone sites in comparison with native bone (p < 0.01). CONCLUSION: The result of this study suggests that overall implant survival as part of the routine oral rehabilitation is high in this patient cohort; however, implant failure was found to be statistically higher for implant placed into transported bone in comparison to native bone.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".