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Record W1640770146 · doi:10.3747/co.v13i6.124

Recommendations of the Canadian Consensus Group on the Management of Chronic Myeloid Leukemia

2006· article· en· W1640770146 on OpenAlexaffvenueabout
Pierre Laneuville, Michael J. Barnett, Robert Bélanger, Stephen Couban, Donna L. Forrest, Denis‐Claude Roy, Jeffrey H. Lipton

Bibliographic record

VenueCurrent Oncology · 2006
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsUniversity of TorontoVancouver Hospital and Health Sciences CentreQueen Elizabeth II Health Sciences CentreUniversité de MontréalHôpital Maisonneuve-RosemontPrincess Margaret Cancer CentreVancouver General HospitalUniversity of British ColumbiaBC Cancer AgencyUniversity Health NetworkDalhousie UniversityRoyal Victoria HospitalMcGill University Health Centre
Fundersnot available
KeywordsMedicineMyeloid leukemiaBioinformaticsComputational biologyCancer researchBiology

Abstract

fetched live from OpenAlex

Chronic myelogenous leukemia (cml) is a disease characterized by the expression of Bcr/Abl, an oncogenic protein tyrosine kinase, and by evolution over time from a relatively benign chronic phase to a rapidly fatal cml blast crisis. Until recently, the standard of care included potentially curative therapy with allogeneic stem cell transplantation, available only to a minority (about 10%) of patients, or medical therapy with interferon-α with or without cytarabine, which helped to prolong the chronic phase of the disease in a minority of patients. The availability of imatinib mesylate, a selective inhibitor of Bcr/Abl approved by Health Canada in 2001, has profoundly altered the clinical and laboratory management of cml. This change in practice has been reviewed by the Canadian Consensus Group on the Management of Chronic Myelogenous Leukemia and has resulted in a new set of recommendations for the optimal care of cml patients.

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)0.021
metaresearch head score (Gemma)0.049
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: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.829

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0060.006
Science and technology studies0.0050.003
Scholarly communication0.0030.002
Open science0.0090.003
Research integrity0.0100.013
Insufficient payload (model declined to judge)0.0080.003

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 head0.064
GPT teacher head0.342
Teacher spread0.277 · 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
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

Citations11
Published2006
Admission routes3
Has abstractyes

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