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Record W3118735017 · doi:10.1016/j.bbmt.2020.06.011

Summary of the 2019 Blood and Marrow Transplant Clinical Trials Network Myeloma Intergroup Workshop on Minimal Residual Disease and Immune Profiling

2020· review· en· W3118735017 on OpenAlexaff
Sarah A. Holstein, Alan Howard, David Avigan, Manisha Bhutani, Adam D. Cohen, Luciano J. Costa, Madhav V. Dhodapkar, Francesca Gay, Nicole Gormley, Damian J. Green, Jens Hillengaß, Neha Korde, Zihai Li, Sham Mailankody, Paola Neri, Samir Parekh, Marcelo C. Pasquini, Noemí Puig, G. David Roodman, Mehmet Samur, Nina Shah, Urvi A. Shah, Qian Shi, Andrew Spencer, Vera J. Suman, Saad Z. Usmani, Philip L. McCarthy

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2020
Typereview
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity of Calgary
FundersNational Center for Advancing Translational SciencesNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of HealthNational Cancer InstituteNational Heart, Lung, and Blood InstituteU.S. Department of Veterans Affairs
KeywordsMedicineMinimal residual diseaseMultiple myelomaClinical trialHematologyOncologyImmune systemDiseaseProfiling (computer programming)Bone transplantationBone marrowImmunologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

The Blood and Marrow Transplant Clinical Trials Network (BMT CTN) Myeloma Intergroup has organized an annual workshop focused on minimal residual disease (MRD) testing and immune profiling (IP) in multiple myeloma since 2016. In 2019, the workshop took place as an American Society of Hematology (ASH) Friday Scientific Workshop titled "Immune Profiling and Minimal Residual Disease Testing in Multiple Myeloma." This workshop focused on 4 main topics: the molecular and immunologic evolution of plasma cell disorders, development of new laboratory- and imaging-based MRD assessment approaches, chimeric antigen receptor T cell therapy research, and statistical and regulatory issues associated with novel clinical endpoints. In this report, we provide a summary of the workshop and discuss future directions.

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)0.004
metaresearch head score (Gemma)0.005
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: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0160.007

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 head0.084
GPT teacher head0.389
Teacher spread0.304 · 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
GenreReview

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

Citations6
Published2020
Admission routes1
Has abstractno

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