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Record W3110880763 · doi:10.2967/jnumed.120.253450

Mars Shot for Nuclear Medicine, Molecular Imaging, and Molecularly Targeted Radiopharmaceutical Therapy

2020· review· en· W3110880763 on OpenAlexaff
Richard L. Wahl, Panithaya Chareonthaitawee, Bonnie Clarke, Alexander Drzezga, Liza Lindenberg, Arman Rahmim, James T. Thackeray, Gary A. Ulaner, Wolfgang Weber, Katherine Zukotynski, John J. Sunderland

Bibliographic record

VenueJournal of Nuclear Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsUniversity of British ColumbiaMcMaster UniversityBC Cancer Agency
FundersSociety of Nuclear Medicine and Molecular Imaging
KeywordsMolecular imagingMedicineValue (mathematics)Medical physicsEngineering ethicsComputer scienceEngineering

Abstract

fetched live from OpenAlex

The Society of Nuclear Medicine and Molecular Imaging created the Value Initiative in 2017 as a major component of its strategic plan to further demonstrate the value of molecular imaging and molecularly targeted radiopharmaceutical therapy to patients, physicians, payers, and funding agencies. The research and discovery domain, 1 of 5 under the Value Initiative, has a goal of advancing the research and development of diagnostic and therapeutic nuclear medicine. Research and discovery efforts and achievements are essential to ensure a bright future for NM and to translate science to practice. Given the remarkable progress in the field, leaders from the research and discovery domain and society councils identified 5 broad areas of opportunity with potential for substantive growth and clinical impact. This article discusses these 5 growth areas, identifying specific areas of particularly high importance for future study and development. As there was an understanding that goals should be both visionary yet achievable, this effort was called the Mars shot for nuclear medicine.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.415
Teacher spread0.328 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations20
Published2020
Admission routes1
Has abstractyes

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