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Record W4283065589 · doi:10.1148/rg.210162

Non-Hodgkin Lymphoma Imaging Spectrum in Children, Adolescents, and Young Adults

2022· article· en· W4283065589 on OpenAlexaff
Eman Marie, María Navallas, Douglas S. Katz, Elnaz Farajirad, Angela Punnett, Sunit Davda, Amer Shammas, Kamaldine Oudjhane, Reza Vali

Bibliographic record

VenueRadiographics · 2022
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsMcMaster Children's Hospital
Fundersnot available
KeywordsMedicineLymphomaMedical imagingClinical trialClinical PracticeModalitiesHodgkin lymphomaRadiologyMedical physicsHealth careFamily medicinePathology

Abstract

fetched live from OpenAlex

In children, adolescents, and young adults (CAYA), non-Hodgkin lymphoma (NHL) is characterized by various age-related dissimilarities in tumor aggressiveness, prevailing pathologic subtypes, and imaging features, as well as potentially different treatment outcomes. Understanding the imaging spectrum of NHL in CAYA with particular attention to children and adolescents is critical for radiologists to support the clinical decision making by the treating physicians and other health care practitioners. The authors discuss the currently performed imaging modalities including radiography, US, CT, MRI, and PET in the diagnosis, staging, and assessment of the treatment response. Familiarity with diagnostic imaging challenges during image acquisition, processing, and interpretation is required when managing patients with NHL. The authors describe potentially problematic and life-threatening scenarios that require prompt management. Moreover, the authors address the unprecedented urge to understand the imaging patterns of possible treatment-related complications of the therapeutic agents used in NHL clinical trials and in practice. Online supplemental material is available for this article. ©RSNA, 2022

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.211
Teacher spread0.207 · 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.

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

Citations13
Published2022
Admission routes1
Has abstractyes

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