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Record W2950424869 · doi:10.1002/pbc.27827

Management of chronic myeloid leukemia in children and adolescents: Recommendations from the Children's Oncology Group CML Working Group

2019· review· en· W2950424869 on OpenAlexaff
Uma H. Athale, Nobuko Hijiya, Briana C. Patterson, John Bergsagel, Jeffrey R. Andolina, Henrique Bittencourt, Kirk R. Schultz, Michael J. Burke, Michele S. Redell, E. Anders Kolb, Donna L. Johnston

Bibliographic record

VenuePediatric Blood & Cancer · 2019
Typereview
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsChildren's Hospital of Eastern OntarioBC Children's HospitalCentre Hospitalier Universitaire Sainte-JustineHamilton Health SciencesMcMaster Children's Hospital
FundersNational Cancer InstituteNational Institutes of Health
KeywordsMedicineMyeloid leukemiaDiseaseLeukemiaPediatricsInternal medicineOncology

Abstract

fetched live from OpenAlex

Chronic myeloid leukemia (CML) accounts for 2-3% of leukemias in children under 15 and 9% in adolescents aged 15-19. The diagnosis and management of CML in children, adolescents, and young adults have several differences compared to that in adults. This review outlines the diagnosis and management of the underlying disease as well as challenges that can occur when dealing with CML in this patient population.

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)0.001
metaresearch head score (Gemma)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.022
GPT teacher head0.307
Teacher spread0.285 · 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

Citations86
Published2019
Admission routes1
Has abstractyes

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