Early Imaging Identification of Osteoradionecrosis and Classification Using the Novel ClinRad System: Results from A Retrospective Observational Cohort
Bibliographic record
Abstract
Abstract Objective Osteoradionecrosis of the jaw (ORNJ) is a chronic radiation-associated toxicity that lacks standardized classification criteria and treatment guidelines. Understanding early signs of tissue injury could help us better predict, prevent, and conservatively manage ORN. Our primary aims were to identify initial clinically-detected signs of ORN, determine the frequency of imaging-detected ORNJ, and validate the ability to classify cases using the novel system, ClinRad. Study Design A retrospective electronic health record review of 91 patients treated for head and neck cancer at The University of Texas MD Anderson Cancer Center with suspected ORN was performed by an Oral Medicine specialist to identify initial signs of ORN. Patients who received reirradiation to the head and neck or did not have enough evidence of ORN were excluded. A descriptive analysis was performed. Results 51 patients met the inclusion criteria. Half (53%) presented with imaging findings and exposed bone. Imaging findings in the absence of bone exposure were identified in 37%, of which disease progression was observed in 26%. All cases were classifiable using ClinRad. Conclusion Subclinical signs of bony changes consistent with ORN may be evident on imaging without exposed bone, supporting the use of imaging surveillance. ClinRad provided a mechanism to classify all cases at early onset. Data availability statement Anonymized data for the reported analyses is made publicly available on figshare at 10.6084/m9.figshare.28292186. Reporting guideline compliance statement In accordance with the EQUATOR Network (Enhancing the QUAlity and Transparency Of health Research) guidance, we have utilized the RECORD checklist, a guideline for the “REporting of studies Conducted using Observational Routinely-collected health data” (Benchimool El at al., 2015) The RECORD checklist is provided as a Supplementary file and available via 10.6084/m9.figshare.28292219. Data was anonymized in accordance with the EQUATOR guideline “Preparing raw clinical data for publication: guidance for journal editors, authors, and peer reviewers” (Hrynaszkiewicz I et al., 2010).
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".