Implementation of the 2024 ASCO guidelines for the prevention and management of osteoradionecrosis in patients with head & neck cancer treated with radiation therapy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Osteoradionecrosis may often be prevented in context of interprofessional healthcare that includes dental specialists prior to and following completion of the patient's head and neck radiation therapy. Important factors, however, compromise delivery of guideline-concordant management of osteoradionecrosis (ORN), including patient access to this interprofessional care. This review is directed to these and related issues, in order to foster enhanced approaches for ORN management. RECENT FINDINGS: The review is centered in the 2024 Journal of Clinical Oncology publication 'Prevention and Management of Osteoradionecrosis in Patients With Head and Neck Cancer Treated With Radiation Therapy: ISOO-MASCC-ASCO Guideline', and the companion 2024 JCO Oncology Practice publication in which clinical insights for the guideline are addressed. Key recent literature is cited in order to provide contemporary context to clinical decision-making for prevention and early diagnosis and treatment of ORN. Although a relatively infrequent complication in head and neck radiation patients, ORN can have profound clinical and financial impact when it occurs. SUMMARY: Interprofessional oncology care is essential for ORN management. Future research is needed in order to enhance this management, including studies directed to predicting risk of development of ORN based on patient-centered risk factors.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".