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Record W2162628440 · doi:10.2967/jnumed.112.112177

Reporting Guidance for Oncologic <sup>18</sup>F-FDG PET/CT Imaging

2013· article· en· W2162628440 on OpenAlex
Ryan D. Niederkohr, Bennett S. Greenspan, John O. Prior, Heiko Schöder, Marc Seltzer, Katherine Zukotynski, Eric Rohren

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

VenueJournal of Nuclear Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsUniversity of Toronto
FundersSociety of Nuclear Medicine and Molecular Imaging
KeywordsDocumentationMedical physicsModality (human–computer interaction)MedicineMedical imagingNuclear medicineRadiologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The written report (or its electronic counterpart) is the primary mode of communication between the physician interpreting an imaging study and the referring physician. The content of this report not only influences patient management and clinical outcomes but also serves as legal documentation of services provided and can be used to justify medical necessity, billing accuracy, and regulatory compliance. Generating a high-quality PET/CT report is perhaps more challenging than generating a report for other imaging studies because of the complexity of this hybrid imaging modality. This article discusses the essential elements of a concise and complete oncologic (18)F-FDG PET/CT report and illustrates these elements through examples taken from routine clinical practice.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.009
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.069
GPT teacher head0.385
Teacher spread0.316 · 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