The Influence of Radiation Therapy on Dental Implantation in Head and Neck Cancer Patients
Bibliographic record
Abstract
Radiotherapy is used to treat patients with head and neck cancers as a primary therapy or as an adjuvant to surgery or chemotherapy. Irradiation results in several complications that can be very overwhelming to the patient. Frequently there is loss of function due to tooth loss, compromised aesthetics, pain and discomfort from xerostomia and mucositis, it also significantly impacts the quality of life. A major advance in dentistry is the successful rehabilitation and replacement of lost teeth by osseointegrated implants. However, the risk of osteoradionecrosis and failure of osseointegration are barriers to implant therapy for those irradiated patients. The aim of this review article is to primarily find out whether the radiotherapy used in the treatment of head and neck cancer patients can affect the success and survival of dental implants according to different studies, and also, to highlight some other pertinent factors that may concurrently influence these implantation. The primary outcome measure shows implants survival in irradiated patients. Most of the studies reported that dental implants can osseointegrate and remain functionally stable in irradiated patients following oral cancer surgery. Accordingly, rehabilitation using dental implants is a viable option for head and neck cancer patients receiving radiotherapy. However, all studies included indicated that survival was significantly higher in non-irradiated patients. Factors such as the mode of radiation therapy delivery, gender, age, implant site and radiation dose at the implant site can affect the survival of dental implant. More research and randomized controlled trails are needed for more accurate judgment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".