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Record W2774576524 · doi:10.1016/j.ijrobp.2017.12.013

American Association of Physicists in Medicine Task Group 263: Standardizing Nomenclatures in Radiation Oncology

2017· review· en· W2774576524 on OpenAlex
Charles S. Mayo, Jean M. Moran, Walter Bosch, Ying Xiao, Todd McNutt, Richard A. Popple, Jeff M. Michalski, Mary Feng, Lawrence B. Marks, Clifton D. Fuller, Ellen Yorke, Jatinder Palta, Peter Gabriel, A Molineu, M.M. Matuszak, Elizabeth Covington, Kathryn Masi, Susan Richardson, Timothy A. Ritter, Tomasz Morgaś, Stella Flampouri, Lakshmi Santanam, Joseph A. Moore, Thomas G. Purdie, Robert C. Miller, Coen Hurkmans, Judy Adams, Qing-Rong Jackie Wu, Colleen Fox, R Siochi, Norman L. Brown, Wilko F.A.R. Verbakel, Yves Archambault, Steven J. Chmura, André Dekker, Don G. Eagle, Thomas J. FitzGerald, Theodore S. Hong, Rishabh Kapoor, Beth Lansing, Shruti Jolly, Mary E. Napolitano, J. F. PERCY, M Rose, Salim Siddiqui, Christof Schadt, William E. Simon, William L. Straube, Sara St. James, Kenneth Ulin, Sue S. Yom, Torunn I. Yock

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Radiation Oncology*Biology*Physics · 2017
Typereview
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsPrincess Margaret Cancer Centre
FundersNational Institute of General Medical SciencesNational Cancer Institute
KeywordsMedical physicsTask groupMedicineRadiation oncologyMultidisciplinary approachRadiation oncologistClinical trialOncologyInternal medicineRadiation therapyEngineering management

Abstract

fetched live from OpenAlex

A substantial barrier to the single- and multi-institutional aggregation of data to supporting clinical trials, practice quality improvement efforts, and development of big data analytics resource systems is the lack of standardized nomenclatures for expressing dosimetric data. To address this issue, the American Association of Physicists in Medicine (AAPM) Task Group 263 was charged with providing nomenclature guidelines and values in radiation oncology for use in clinical trials, data-pooling initiatives, population-based studies, and routine clinical care by standardizing: (1) structure names across image processing and treatment planning system platforms; (2) nomenclature for dosimetric data (eg, dose-volume histogram [DVH]-based metrics); (3) templates for clinical trial groups and users of an initial subset of software platforms to facilitate adoption of the standards; (4) formalism for nomenclature schema, which can accommodate the addition of other structures defined in the future. A multisociety, multidisciplinary, multinational group of 57 members representing stake holders ranging from large academic centers to community clinics and vendors was assembled, including physicists, physicians, dosimetrists, and vendors. The stakeholder groups represented in the membership included the AAPM, American Society for Radiation Oncology (ASTRO), NRG Oncology, European Society for Radiation Oncology (ESTRO), Radiation Therapy Oncology Group (RTOG), Children's Oncology Group (COG), Integrating Healthcare Enterprise in Radiation Oncology (IHE-RO), and Digital Imaging and Communications in Medicine working group (DICOM WG); A nomenclature system for target and organ at risk volumes and DVH nomenclature was developed and piloted to demonstrate viability across a range of clinics and within the framework of clinical trials. The final report was approved by AAPM in October 2017. The approval process included review by 8 AAPM committees, with additional review by ASTRO, European Society for Radiation Oncology (ESTRO), and American Association of Medical Dosimetrists (AAMD). This Executive Summary of the report highlights the key recommendations for clinical practice, research, and trials.

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.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.428
Teacher spread0.396 · 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