International Expert-Based Consensus Definition, Staging Criteria, and Minimum Data Elements for Osteoradionecrosis of the Jaw: An Inter-Disciplinary Modified Delphi Study
Bibliographic record
Abstract
Purpose: Osteoradionecrosis of the jaw (ORNJ) is a severe iatrogenic disease characterized by bone death after radiation therapy (RT) to the head and neck. With over 9 published definitions and at least 16 diagnostic/staging systems, the true incidence and severity of ORNJ are obscured by lack of a standard for disease definition and severity assessment, leading to inaccurate estimation of incidence, reporting ambiguity, and likely under-diagnosis worldwide. This study aimed to achieve consensus on an explicit definition and phenotype of ORNJ and related precursor states through data standardization to facilitate effective diagnosis, monitoring, and multidisciplinary management of ORNJ. Methods: The ORAL Consortium comprised 69 international experts, including representatives from medical, surgical, radiation oncology, and oral/dental disciplines. Using a web-based modified Delphi technique, panelists classified descriptive cases using existing staging systems, reviewed systems for feature extraction and specification, and iteratively classified cases based on clinical/imaging feature combinations. Results: The Consortium ORNJ definition was developed in alignment with SNOMED-CT terminology and recent ISOO-MASCC-ASCO guideline recommendations. Case review using existing ORNJ staging systems showed high rates of inability to classify (up to 76%). Ten consensus statements and nine minimum data elements (MDEs) were outlined for prospective collection and classification of precursor/ORNJ stages. Conclusion: This study provides an international, consensus-based definition and MDE foundation for standardized ORNJ reporting in cancer survivors treated with RT. Head and neck surgeons, radiation, surgical, medical oncologists, and dental specialists should adopt MDEs to enable scalable health information exchange and analytics. Work is underway to develop both a human- and machine-readable knowledge representation for ORNJ (i.e., ontology) and multidisciplinary resources for dissemination to improve ORNJ reporting in academic and community practice settings.
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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 | 0.177 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".