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Record W2744179443 · doi:10.2147/vhrm.s108874

Cardiovascular toxicities of BCR-ABL tyrosine kinase inhibitors in chronic myeloid leukemia: preventive strategies and cardiovascular surveillance

2017· review· en· W2744179443 on OpenAlexaff
Nazanin Aghel, Diego Delgado, Jeffrey H. Lipton

Bibliographic record

VenueVascular Health and Risk Management · 2017
Typereview
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsPrincess Margaret Cancer CentreToronto General HospitalUniversity of TorontoUniversity Health Network
FundersPrincess Margaret Hospital Foundation
KeywordsMedicineMyeloid leukemiaTyrosine kinaseTyrosine-kinase inhibitorImmunologyInternal medicineCancerReceptor

Abstract

fetched live from OpenAlex

Abstract: Tyrosine kinase inhibitors (TKIs) have revolutionized the treatment and outcomes of chronic myeloid leukemia (CML). Despite their significant impact on the management of CML, there is growing evidence that TKIs may cause cardiovascular and/or metabolic complications. In this review, we present the current evidence regarding the cardiovascular safety profiles of BCR-ABL TKIs. Methodological challenges of studies that reported the cardiovascular safety of TKIs are discussed. We also propose management strategies for cardiovascular surveillance and risk factor modification during treatment with these agents. Keywords: BCR-ABL, tyrosine kinase inhibitor, peripheral arterial disease, cardiovascular disease, adverse event

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.306
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
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

Citations84
Published2017
Admission routes1
Has abstractyes

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