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Record W2518117171 · doi:10.1080/17474086.2016.1232163

Ponatinib in the therapy of chronic myeloid leukemia

2016· review· en· W2518117171 on OpenAlex
Marc Poch Martell, Hassan Sibai, Uday Deotare, Jeffrey H. Lipton

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueExpert Review of Hematology · 2016
Typereview
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
Fundersnot available
KeywordsPonatinibMedicineMagic bulletMyeloid leukemiaTargeted therapyDiseaseOncologyTyrosine-kinase inhibitorNilotinibInternal medicineImmunologyBioinformaticsImatinibCancer

Abstract

fetched live from OpenAlex

INTRODUCTION: Chronic Myeloid Leukemia (CML) is a myeloproliferative disorder that has become the neoplastic poster child for understanding the disease biology of a malignant disease and targeting effective therapy. The targeted therapy of BCR-ABL inhibition by tyrosine kinase inhibitors (TKI) has provided the epitome for "Ehlrich's magic bullet" postulated decades ago. AREAS COVERED: Due to the therapy with these drugs, the survival of newly diagnosed patients with this disease now approaches that of age matched controls. Progression to advanced phases of CML had decreased over the years, though resistance has now been increasingly identified. Expert commentary: Ponatinib is a third generation TKI, which has shown to be effective in both early and advanced phases of CML and those bearing resistant mutations, specifically T315I. However, new side effect considerations need to be balanced with the efficacy, to establish the role of ponatinib in the therapy of CML.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.838
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.388
Teacher spread0.345 · 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