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Record W1525555980 · doi:10.1038/leu.2015.168

BCR-ABL1 mutation development during first-line treatment with dasatinib or imatinib for chronic myeloid leukemia in chronic phase

2015· article· en· W1525555980 on OpenAlexaff
Timothy P. Hughes, Giuseppe Saglio, Alfonso Quintás‐Cardama, Michael J. Mauro, D-W Kim, Jeffrey H. Lipton, M. Brigid Bradley‐Garelik, Jon Ukropec, Andreas Hochhaus

Bibliographic record

VenueLeukemia · 2015
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsDasatinibImatinibMedicineNilotinibInternal medicineMyeloid leukemiaTyrosine-kinase inhibitorOncologyImatinib mesylateMutationBiologyCancerGenetics

Abstract

fetched live from OpenAlex

BCR-ABL1 mutations are a common, well-characterized mechanism of resistance to imatinib as first-line treatment of chronic myeloid leukemia in chronic phase (CML-CP). Less is known about mutation development during first-line treatment with dasatinib and nilotinib, despite increased use because of higher response rates compared with imatinib. Retrospective analyses were conducted to characterize mutation development in patients with newly diagnosed CML-CP treated with dasatinib (n=259) or imatinib (n=260) in DASISION (Dasatinib versus Imatinib Study in Treatment-Naive CML-CP), with 3-year minimum follow-up. Mutation screening, including patients who discontinued treatment and patients who had a clinically relevant on-treatment event (no confirmed complete cytogenetic response (cCCyR) and no major molecular response (MMR) within 12 months; fivefold increase in BCR-ABL1 with loss of MMR; loss of CCyR), yielded a small number of patients with mutations (dasatinib, n=17; imatinib, n=18). Dasatinib patients had a narrower spectrum of mutations (4 vs 12 sites for dasatinib vs imatinib), fewer phosphate-binding loop mutations (1 vs 9 mutations), fewer multiple mutations (1 vs 6 patients) and greater occurrence of T315I (11 vs 0 patients). This trial was registered at www.clinicaltrials.gov as NCT00481247.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.322
Teacher spread0.275 · 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
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

Citations72
Published2015
Admission routes1
Has abstractyes

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