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Record W2682865355 · doi:10.1097/mcp.0000000000000412

Pulmonary arterial hypertension induced by tyrosine kinase inhibitors

2017· review· en· W2682865355 on OpenAlexaff
Jason Weatherald, Marie‐Camille Chaumais, David Montani

Bibliographic record

VenueCurrent Opinion in Pulmonary Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsUniversity of Calgary
FundersAgence Nationale de Sécurité du Médicament et des Produits de Santé
KeywordsMedicineDasatinibDiscontinuationBosutinibPonatinibTyrosine kinaseInternal medicineImatinibPulmonary hypertensionTyrosine-kinase inhibitorPharmacologyCardiologyCancerMyeloid leukemia

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Tyrosine kinase inhibitors (TKIs) have revolutionized the treatment of several neoplastic conditions; however, pulmonary arterial hypertension (PAH) has been reported as a complication of TKIs, predominantly with dasatinib. Recent studies have elucidated the potential mechanisms of TKI-induced PAH and have better clarified the long-term outcomes. RECENT FINDINGS: In addition to the known association between dasatinib and PAH, several other TKIs have recently been reported to cause PAH, including ponatinib, bosutinib and lapatinib. Dasatinib causes direct pulmonary artery endothelial cell toxicity through the production of mitochondrial reactive oxygen species, but likely requires the presence of a second risk factor to cause PAH. Symptoms and haemodynamic abnormalities frequently resolve after discontinuation of the TKI, but PAH persists in over a third of patients and can reoccur when other TKIs are used, which warrants close follow-up. Rare fatal cases have occurred; therefore, treatment with PAH-specific therapy is recommended for patients with right heart failure or persistent PAH after discontinuation of the TKI. SUMMARY: PAH is a rare but important complication of several TKIs. Management includes discontinuation of the TKI, close follow-up and PAH-specific therapy in severe cases.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.183
GPT teacher head0.409
Teacher spread0.226 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations43
Published2017
Admission routes1
Has abstractyes

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