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Record W2870342969 · doi:10.1002/mp.13087

Redefining and reinvigorating the role of physics in clinical medicine: A Report from the <scp>AAPM</scp> Medical Physics 3.0 Ad Hoc Committee

2018· article· en· W2870342969 on OpenAlexaff
Ehsan Samei, Todd Pawlicki, Daniel Bourland, Erika Chin, Shiva K. Das, Mary Kay Fox, D. Jay Freedman, Nicholas J. Hangiandreou, David W. Jordan, Melissa Martin, Robin Miller, William Pavlicek, Daniel Pavord, Lisa Schober, Bruce Thomadsen, Brendan Whelan

Bibliographic record

VenueMedical Physics · 2018
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsExpansiveExcellenceHealth careMedical physicistEngineering ethicsHealth physicsMedicinePhysicsMedical educationPolitical scienceMedical physicsEngineeringNuclear medicine

Abstract

fetched live from OpenAlex

Derived from 2 yr of deliberations and community engagement, Medical Physics 3.0 (MP3.0) is an effort commissioned by the American Association of Physicists in Medicine (AAPM) to devise a framework of strategies by which medical physicists can maintain and improve their integral roles in, and contributions to, health care and its innovation under conditions of rapid change and uncertainty. Toward that goal, MP3.0 advocates a broadened and refreshed model of sustainable excellence by which medical physicists can and should contribute to health care. The overarching conviction of MP3.0 is that every healthcare facility can benefit from medical physics and every patient's care can be improved by a medical physicist. This large and expansive challenge necessitates a range of strategies specific to each area of medical physics: clinical practice, research, product development, and education. The present paper offers a summary of the Phase 1 deliberations of the MP3.0 initiative pertaining to strategic directions of the discipline primarily but not exclusively oriented toward the clinical practice of medical physics in the United States.

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)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.006
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.036
GPT teacher head0.395
Teacher spread0.359 · 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 designOther design
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

Citations36
Published2018
Admission routes1
Has abstractyes

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