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Refining risk classification in childhood B acute lymphoblastic leukemia: results of DFCI ALL Consortium Protocol 05-001

2018· article· en· W2811286257 on OpenAlexaff
Lynda M. Vrooman, Traci M. Blonquist, Marian H. Harris, Kristen E. Stevenson, Andrew E. Place, Sarah K. Hunt, Jane E. O’Brien, Barbara L. Asselin, Uma H. Athale, Luis A. Clavell, Peter D. Cole, Kara M. Kelly, Caroline Laverdière, Jean‐Marie Leclerc, Bruno Michon, Marshall A. Schorin, Maria Luisa Sulis, Jennifer Welch, Donna Neuberg, Stephen E. Sallan, Lewis B. Silverman

Bibliographic record

VenueBlood Advances · 2018
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsUniversité LavalCentre hospitalier universitaire de QuébecUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineMcMaster University
FundersNational Cancer InstituteNational Institutes of HealthEnzon Pharmaceuticals
KeywordsMedicineLymphoblastic LeukemiaProtocol (science)OncologyInternal medicineLeukemiaPediatricsPathology

Abstract

fetched live from OpenAlex

Key Points Childhood B-ALL patients, including those with VHR features, had favorable outcomes on DFCI 05-001 risk-stratified therapy. IKZF1 deletion was an independent predictor of inferior outcome, including among patients with low end-induction MRD.

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)0.037
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.055
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0050.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.004

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 head0.020
GPT teacher head0.324
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 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

Citations115
Published2018
Admission routes1
Has abstractyes

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