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Record W4410431838 · doi:10.1183/23120541.00382-2025

Drugs and toxins associated with pulmonary arterial hypertension: from established culprits to novel threats

2025· review· en· W4410431838 on OpenAlexaff
Julien Grynblat, Alex Hlavaty, Laurent Savale, Jason Weatherald, Antoine Le Bozec, Benoit Aguado, Marie‐Camille Chaumais, Marc Humbert, Charles Khouri, David Montani

Bibliographic record

VenueERJ Open Research · 2025
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicinePulmonary hypertensionIntensive care medicinePharmacologyCardiology

Abstract

fetched live from OpenAlex

Pulmonary arterial hypertension (PAH) is a rare form of precapillary pulmonary hypertension. PAH may be idiopathic, heritable, associated with features of pulmonary veno-occlusive disease and/or pulmonary capillary haemangiomatosis, or linked to drug and toxin exposure. Since the first identification of PAH cases associated with drugs and toxins >50 years ago, the number of suspected agents has significantly increased. Following the 6th World Symposium on Pulmonary Hypertension (2018), drugs and toxins have been categorised based on their strength of association with PAH, with nine agents currently classified as definitely associated and 16 as possibly associated. At the 7th World Symposium on Pulmonary Hypertension (2024), carfilzomib and mitomycin C were added as definitely associated with PAH, while bevacizumab and bortezomib were considered as possibly associated with PAH. In cases of suspected PAH following drug or toxin exposure, specific measures are required, including pharmacovigilance reporting, echocardiography, right heart catheterisation to confirm the diagnosis and potential initiation of targeted treatment. Here, we review the latest updates on PAH associated with drugs and toxins.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.003
Research integrity0.0010.002
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 head0.198
GPT teacher head0.439
Teacher spread0.241 · 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 teacher head, not a consensus.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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