Survival of osseointegrated implants in head and neck cancer patients submitted to multimodal treatment: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Purpose: to investigate the survival rate in implants placement in irradiated and non-irradiated bone in patients undergoing head and neck cancer (HNC) treatment. We focused on the consequences of the main complications, such as osteoradionecrosis, peri-implantitis. Methods: An electronic search conducted by PRISMA protocol was performed. Full texts were carefully assessed, and data were assimilated into a tabular form for discussion and consensus among the expert panel. The quality assessment and the risk of bias are verified by Joanna Briggs Institute checklist (JBI) and The Newcastle-Ottawa Scale (NOS), and Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) assessment tool. Results: A total of 452 records were identified in the based on our PICOs strategy and after screening, 19 articles were included in the descriptive analysis of the review. Totaling 473 implants placed in irradiated and non-irradiated bone, and 31.6 % of the patients were over 60 years of age. 57.9%) performed implant placement in a period of 12 months or more after the ending of radiotherapy. Only 5 studies had a follow-up period longer than 5 years after implant placement, of which three were used for the meta-analysis. In the meta-analysis of 5-year survival rate analysis of implants in irradiated bone was assessed; a random effect model was used and a weighted proportion (PP) of 93.13% (95% CI: 87.20 - 99.06; p<0.001), and in the 5-year survival rate analysis of implants in non-irradiated bone was analysed, a fixed effect model was used and a weighted proportion (PP) of 98.52% survival (95% CI: 97.56-99.48, p<0.001). Conclusions: Survival rates of implants placed in irradiated bone is clinically satisfactory after a follow-up of 5 years, with a fewer percentage than in implants placed in non-irradiated bone after metanalyses performed.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".