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Record W4394602181 · doi:10.1101/2024.04.07.24305400

International Expert-Based Consensus Definition, Staging Criteria, and Minimum Data Elements for Osteoradionecrosis of the Jaw: An Inter-Disciplinary Modified Delphi Study

2024· preprint· en· W4394602181 on OpenAlexafffund
Amy C. Moreno, Erin Watson, Laia Humbert‐Vidan, Douglas E. Peterson, Lisanne V. van Dijk, Teresa Guerrero Urbano, L. Van den Bosch, Andrew Hope, Matthew S. Katz, Frank Hoebers, Ruth Aponte Wesson, James E. Bates, Paolo Bossi, Adeyinka F. Dayo, Mélanie Dore, Eduardo Rodrigues Fregnani, Thomas J. Galloway, Daphna Y. Gelblum, Issa A. Hanna, Christina Henson, Sudarat Kiat‐amnuay, Anke Korfage, Nancy Y. Lee, Carol M. Lewis, Charlotte Duch Lynggaard, Antti Mäkitie, Marco Magalhaes, Yvonne M. Mowery, Carles Muñoz‐Montplet, Jeffrey N. Myers, Ester Orlandi, Jaymit Patel, Jillian Rigert, Deborah Saunders, Jonathan D. Schoenfeld, Uğur Selek, Efsun Somay, Vinita Takiar, Juliette Thariat, Gerda M. Verduijn, Alessandro Villa, Nick West, Max J. H. Witjes, Alex Won, Mark E. Wong, Christopher M. K. L. Yao, Simon Young, Kamal Al‐Eryani, Carly E. A. Barbon, Doke J.M. Buurman, François J. Dieleman, Theresa M. Hofstede, Abdul Ahad Khan, Adegbenga O. Otun, John C. Robinson, Lauren Hum, Jørgen Johansen, Rajesh V. Lalla, Alexander Lin, Vinod Patel, Richard Shaw, Mark S. Chambers, J. Daniel, Mabi Singh, Noam Yarom, Abdallah Mohamed, Katherine A. Hutcheson, Stephen Y. Lai, Clifton D. Fuller

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldMedicine
TopicOral health in cancer treatment
Canadian institutionsNOSM UniversityPrincess Margaret Cancer CentreUniversity of TorontoHealth Sciences NorthUniversity Health Network
FundersNational Institute of Biomedical Imaging and BioengineeringNational Cancer InstituteCanadian Institutes of Health ResearchEMD SeronoGenentechNational Institutes of HealthKing's College LondonCastle BiosciencesSiemens HealthineersModernaAmgenCardinal HealthAstellas PharmaElektaSanofiUniversity of Texas MD Anderson Cancer CenterPfizerSun PharmaRegeneron PharmaceuticalsCancer Research SocietyCVS HealthBristol-Myers Squibb
KeywordsOsteoradionecrosisMedicineMedical physicsDelphi methodTerminologyRadiation therapyRadiologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.252
GPT teacher head0.458
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2024
Admission routes2
Has abstractyes

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