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Record W2044090992 · doi:10.1126/scitranslmed.3005428

Pulmonary Arterial Hypertension: Challenges in Translational Research and a Vision for Change

2013· review· en· W2044090992 on OpenAlexaff
Gopinath Sutendra, Evangelos D. Michelakis

Bibliographic record

VenueScience Translational Medicine · 2013
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTranslational researchMedicinePulmonary hypertensionIntensive care medicineCardiologyDiseaseInternal medicinePathology

Abstract

fetched live from OpenAlex

Pulmonary arterial hypertension (PAH) is a vascular remodeling disease with a relentless course toward heart failure and early death. Existing PAH therapies, all of which were developed originally to treat systemic vascular diseases, cannot reverse the disease or markedly improve survival and are expensive. Although there has been a recent increase in the number of potential new therapies emerging from animal studies, less than 3% of the active PAH clinical trials are examining such therapies. There are many potential explanations for the translational gap in this complex multifactorial disease. We discuss these challenges and propose solutions that range from including clinical endpoints in animal studies and improving the rigor of human trials to conducting mechanistic early-phase trials and randomized trials with innovative designs based on personalized medicine principles. Global, independent patient and tissue registries and enhanced communication among academics, industry, and regulatory authorities are needed. The diversity of the mechanisms and pathology of PAH calls for broad comprehensive theories that encompass emerging evidence for contributions of metabolism and inflammation to PAH to support more effective therapeutic target identification.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.518
GPT teacher head0.503
Teacher spread0.015 · 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 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

Citations79
Published2013
Admission routes1
Has abstractyes

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