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Record W2895187195 · doi:10.21693/1933-088x-17.2.69

Tyrosine Kinase Inhibitor–Induced Pulmonary Arterial Hypertension

2018· article· en· W2895187195 on OpenAlexaff
Mariana Preda, Andrei Seferian, Etienne‐Marie Jutant, Marie‐Camille Chaumais, Laurent Savale, Xavier Jaïs, Jason Weatherald, Olivier Sitbon, Marc Humbert, David Montani

Bibliographic record

VenueAdvances in Pulmonary Hypertension · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBosutinibMedicineDasatinibPonatinibImatinibTyrosine-kinase inhibitorPulmonary hypertensionTyrosine kinaseImatinib mesylateNilotinibInternal medicineCardiologyMyeloid leukemiaPharmacologyReceptor

Abstract

fetched live from OpenAlex

The treatment of the malignant hematological diseases has been revolutionized by the use of tyrosine kinase inhibitors (TKI): for example, imatinib in patients with chronic myeloid leukemia. Dasatinib, a second-generation TKI, has been reported to induce severe pulmonary arterial hypertension (PAH). The mechanism of PAH development is presumed to be endothelial cell toxicity through the production of mitochondrial reactive oxygen species. There are other TKIs that are reported to cause PAH, such as: ponatinib, bosutinib, lapatinib, and lorlatinib. The management of PAH due to TKIs primarily involves stopping the TKI treatment, which can lead to clinical and hemodynamic normalization. A third of the patients who develop PAH can have persistent symptoms of dyspnea and right heart failure even after the interruption of the TKIs. For these patients, use of specific PAH treatment is indicated along with close follow-up. In rare cases, TKI-induced PAH can be fatal. Thus, early screening for PAH diagnosis and proper management is required.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.272
Teacher spread0.250 · 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 designCase report
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

Citations4
Published2018
Admission routes1
Has abstractyes

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