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Record W2744041560 · doi:10.1038/leu.2017.247

Sustained deep molecular responses in patients switched to nilotinib due to persistent BCR-ABL1 on imatinib: final ENESTcmr randomized trial results

2017· article· en· W2744041560 on OpenAlexaff
Timothy P. Hughes, Brian Leber, Francisco Cervantes, Nelson Spector, Ricardo Pasqüini, Nelma Clementino, Anthony P. Schwarer, P.E. Dorlhiac‐Llacer, F-X Mahon, Delphine Réa, A. Guerci-Bresler, Suzanne Kamel‐Reid, Israel Bendit, Sandip Acharya, Tara Glynos, Darshan Dalal, Susan Branford, J. H. Lipton

Bibliographic record

VenueLeukemia · 2017
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsPrincess Margaret Cancer CentreToronto General HospitalUniversity Health NetworkUniversity of TorontoMcMaster University
FundersNovartis Pharmaceuticals Corporation
KeywordsNilotinibImatinibMedicineRandomized controlled trialOncologyInternal medicineMyeloid leukemia

Abstract

fetched live from OpenAlex

For patients with chronic myeloid leukemia in chronic phase (CML-CP), treatment with a BCR-ABL1 tyrosine kinase inhibitor (TKI), such as imatinib or nilotinib, can result in high response rates and near-normal life expectancies. 1 , 2 , 3 Although most patients treated with imatinib achieve a complete cytogenetic response (CCyR) and many achieve a major molecular response (MMR; BCR-ABL1 ⩽ 0.1% on the International Scale ( BCR-ABL1 IS )), MR 4 ( BCR-ABL1 IS ⩽ 0.01%), or MR 4.5 ( BCR-ABL1 IS ⩽ 0.0032%), 4 , 5 , 6 , 7 , 8 , 9 , 10 results from the Evaluating Nilotinib Efficacy and Safety in Clinical Trials–Newly Diagnosed Patients (ENESTnd) study showed that frontline nilotinib therapy results in higher response rates than frontline imatinib therapy. 9 , 10

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)0.003
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.024
GPT teacher head0.299
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations43
Published2017
Admission routes1
Has abstractyes

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