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Record W2884078688 · doi:10.1097/rlu.0000000000002216

18F-FDG PET/CT With Diffusely High FDG Uptake Throughout Subcutaneous Adipose Tissues

2018· article· en· W2884078688 on OpenAlexaff
Melissa Cindy Kong, Helen Nadel

Bibliographic record

VenueClinical Nuclear Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicBody Contouring and Surgery
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsMedicineAdipose tissueNuclear medicineSubcutaneous fatPositron emission tomographySubcutaneous tissuePositron Emission Tomography-Computed TomographyRadiologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

A 9-year-old girl presented with facial rash, angioedema, fevers, and night sweats. She was diagnosed with chronic active Epstein-Barr virus infection and placed on chronic steroid treatment. F-FDG PET/CT performed 3 weeks following presentation revealed diffuse subcutaneous soft tissue FDG activation throughout the entire body, with likely localization to white subcutaneous adipose tissue. This highly unusual appearance may have been due to the patient being treated with corticosteroids at the time of the scan.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.042
GPT teacher head0.350
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations18
Published2018
Admission routes1
Has abstractyes

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