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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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.890
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 teacher head, not a consensus.

Study designOther design
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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