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Record W4384343266 · doi:10.1002/pbc.30565

Children's Oncology Group's 2023 blueprint for research: Non‐Hodgkin lymphoma

2023· article· en· W4384343266 on OpenAlexaff
Nader Kim El‐Mallawany, Sarah Alexander, Mark Fluchel, Robert J. Hayashi, Eric Lowe, Lisa Giulino‐Roth, Birte Wistinghausen, Michelle L. Hermiston, Carl E. Allen

Bibliographic record

VenuePediatric Blood & Cancer · 2023
Typearticle
Languageen
FieldMedicine
TopicHistiocytic Disorders and Treatments
Canadian institutionsHospital for Sick Children
FundersNational Institutes of HealthNational Cancer InstituteEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentGenentechWeill Cornell Medical CollegeSwedish Orphan Biovitrum
KeywordsMedicineBlueprintLymphomaOncologyInternal medicineHodgkin lymphomaPediatric oncologyLangerhans cell histiocytosisOverall survivalLymphoblastic lymphomaDiseaseCancerImmunologyT cell

Abstract

fetched live from OpenAlex

Pediatric non-Hodgkin lymphoma (NHL) includes over 30 histologies (many with subtypes), with approximately 800 cases per year in the United States. Improvements in survival in NHL over the past 5 decades align with the overall success of the cooperative trial model with dramatic improvements in outcomes. As an example, survival for advanced Burkitt lymphoma is now >95%. Major remaining challenges include survival for relapsed and refractory disease and long-term morbidity in NHL survivors. Langerhans cell histiocytosis (LCH) was added to the NHL Committee portfolio in recognition of LCH as a neoplastic disorder and the tremendous unmet need for improved outcomes. The goal of the Children' Oncology Group NHL Committee is to identify optimal cures for every child and young adult with NHL (and LCH). Further advances will require creative solutions, including engineering study groups to combine rare populations, biology-based eligibility, alternative endpoints, facilitating international collaborations, and coordinated correlative biology.

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)0.000
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 score0.342
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.076
GPT teacher head0.408
Teacher spread0.332 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

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