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Record W2984531433 · doi:10.6004/jnccn.2019.0029

NCCN Guidelines Insights: B-Cell Lymphomas, Version 3.2019

2019· article· en· W2984531433 on OpenAlexaff
Andrew D. Zelenetz, Leo I. Gordon, Jeremy S. Abramson, Ranjana H. Advani, Nancy L. Bartlett, Paolo F. Caimi, Julie Chang, Julio C. Chávez, Beth Christian, Luis Fayad, Martha Glenn, Thomas M. Habermann, Nancy L. Harris, Francisco J. Hernandez‐Ilizaliturri, Mark Kaminski, Chris R. Kelsey, Nadia Khan, Susan Krivacic, Ann S. LaCasce, Amitkumar Mehta, Auayporn Nademanee, Rachel Rabinovitch, Nishitha Reddy, Erin Reid, Kenneth B. Roberts, Stephen D. Smith, Erin D. Snyder, Lode J. Swinnen, Julie M. Vose, Mary A. Dwyer, Hema Sundar

Bibliographic record

VenueJournal of the National Comprehensive Cancer Network · 2019
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsAstraZeneca (Canada)Amgen (Canada)
FundersNational Comprehensive Cancer Network
KeywordsMedicineFollicular lymphomaOncologyLymphomaInternal medicineRefractory (planetary science)Diffuse large B-cell lymphomaCancer researchBiology

Abstract

fetched live from OpenAlex

Diffuse large B-cell lymphomas (DLBCLs) and follicular lymphoma (FL) are the most common subtypes of B-cell non-Hodgkin's lymphomas in adults. Histologic transformation of FL to DLBCL (TFL) occurs in approximately 15% of patients and is generally associated with a poor clinical outcome. Phosphatidylinositol 3-kinase (PI3K) inhibitors have shown promising results in the treatment of relapsed/refractory FL. CAR T-cell therapy (axicabtagene ciloleucel and tisagenlecleucel) has emerged as a novel treatment option for relapsed/refractory DLBCL and TFL. These NCCN Guidelines Insights highlight important updates to the NCCN Guidelines for B-Cell Lymphomas regarding the treatment of TFL and relapsed/refractory FL and DLBCL.

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)0.006
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0040.002
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0330.026

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 head0.055
GPT teacher head0.355
Teacher spread0.300 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations189
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the National Comprehensive Cancer NetworkSame topicCAR-T cell therapy researchFrench-language works237,207