Incidence of osteoradionecrosis of the jaws: A retrospective study of 620 head and neck cancer patients treated by radiotherapy
Bibliographic record
Abstract
Abstract Background This study aims to assess systemic and local risk factors influencing the development of osteoradionecrosis of the jaws (ORN) and its incidence in head and neck cancer patients undergoing radiotherapy.Methods This was a retrospective cohort study of 620 adult patients following radiation for a head and neck cancer in 2011 or 2012. Results Of the 181 patients who did not require any extraction, the incidence of ORN was 0.5%. Of the 266 patients with 1491 dental extractions (mean 5.5 teeth per patient) performed before radiotherapy, the incidence of ORN was 3.7%. ORN was always observed in extraction sites located in the field of radiation. No dental extractions were done during radiotherapy. Of the 20 patients with 53 dental extractions (mean 2.7 teeth per patient) performed after radiotherapy, 15 teeth were located in the field of radiation. No case of ORN was reported in that group. For edentulous patients, ORN incidence was 1.7%. Conclusion Within the limitations of this study, the incidence of ORN can be minimized with a meticulous pre-radiotherapy dental examination, a comprehensive treatment plan and diligent post-radiotherapy follow-ups conducted by an experienced multidisciplinary team.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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".