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Diagnosis and Treatment of Right Heart Failure in Pulmonary Vascular Diseases: A National Heart, Lung, and Blood Institute Workshop

2021· review· en· W3171941656 on OpenAlexaff
Jane A. Leopold, Steven M. Kawut, Micheala A. Aldred, Stephen L. Archer, Raymond L. Benza, Michael R. Bristow, Evan L. Brittain, Naomi C. Chesler, Frances S. DeMan, Serpil C. Erzurum, Mark T. Gladwin, Paul M. Hassoun, Anna R. Hemnes, Tim Lahm, João A.C. Lima, Joseph Loscalzo, Bradley A. Maron, Laura Mercer‐Rosa, John H. Newman, Susan Redline, Stuart Rich, Franz Rischard, Lissa Sugeng, W.H. Wilson Tang, Ryan J. Tedford, Emily J. Tsai, Corey E. Ventetuolo, You‐Yang Zhao, Neil R. Aggarwal, Lei Xiao, Hua Cai, Lu Cai, Xiongwen Chen, Jason M. Elinoff, Benjamin H. Freed, Andrea L. Frump, Kara N. Goss, Michael P. Gray, Wei Huang, Todd M. Kolb, Marc A. Simon, Michael A. Solomon, Edda Spiekerkoetter, Rebecca Vanderpool, Minghui Zou, Yingjie Chen, Adhikari Bishow, Scarlet Shi, Gail Weinmann, Renee Wong

Bibliographic record

VenueCirculation Heart Failure · 2021
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsColumbia CollegeQueen's University
FundersNational Center for Advancing Translational SciencesNational Heart, Lung, and Blood InstituteU.S. Department of Veterans Affairs
KeywordsMedicineVentricleLungCardiologyHeart failureTranslational researchRight heart failureHeart diseasePulmonary heart diseaseInternal medicineDiseaseIntensive care medicineRight heartPathology

Abstract

fetched live from OpenAlex

Right ventricular dysfunction is a hallmark of advanced pulmonary vascular, lung parenchymal, and left heart disease, yet the underlying mechanisms that govern (mal)adaptation remain incompletely characterized. Owing to the knowledge gaps in our understanding of the right ventricle (RV) in health and disease, the National Heart, Lung, and Blood Institute commissioned a working group to identify current challenges in the field. These included a need to define and standardize normal RV structure and function in populations; access to RV tissue for research purposes and the development of complex experimental platforms that recapitulate the in vivo environment; and the advancement of imaging and invasive methodologies to study the RV within basic, translational, and clinical research programs. Specific recommendations were provided, including a call to incorporate precision medicine and innovations in prognosis, diagnosis, and novel RV therapeutics for patients with pulmonary vascular disease.

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)0.002
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.002

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 head0.042
GPT teacher head0.335
Teacher spread0.293 · 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

Citations29
Published2021
Admission routes1
Has abstractyes

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