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Record W4220826192 · doi:10.1002/cpz1.383

Measurable Residual Disease Detection in B‐Acute Lymphoblastic Leukemia: The Children's Oncology Group (COG) Method

2022· article· en· W4220826192 on OpenAlexaff
Michael J. Borowitz, Brent L. Wood, Michael Keeney, Benjamin D. Hedley

Bibliographic record

VenueCurrent Protocols · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMinimal residual diseaseMedicineProtocol (science)Lymphoblastic LeukemiaFlow cytometryImmunophenotypingOncologyLeukemiaPediatric oncologyInternal medicineDiseaseResidualCogImmunologyPathologyCancerComputer science

Abstract

fetched live from OpenAlex

Measurable (minimal) residual disease (MRD) in B-acute lymphoblastic leukemia (B-ALL), as assessed by flow cytometry, is an established prognostic factor used to adjust treatment in most pediatric therapeutic protocols. MRD in B-ALL has been standardized by the Children's Oncology Group in North America and more recently in a multicenter Foundation for the National Institutes of Health-funded study. This article outlines the reagents, instrument setup, and analysis protocols required for the reproducible detection of residual leukemic cells in patients following induction therapy for B-ALL. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Staining and flow cytometry for B-acute lymphoblastic leukemia (B-ALL) measurable residual disease detection Support Protocol: Specimen collection, handling, storage, and shipping Basic Protocol 2: Analysis and interpretation of data for B-ALL measurable residual disease detection Basic Protocol 3: Analysis of samples lacking sufficient CD19+ events.

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)0.005
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.005

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 head0.040
GPT teacher head0.376
Teacher spread0.336 · 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
GenreMethods

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

Citations25
Published2022
Admission routes1
Has abstractyes

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