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Record W2173294927 · doi:10.1586/17476348.2015.1106316

Riociguat for the treatment of pulmonary hypertension

2015· review· en· W2173294927 on OpenAlexaff
Nathan Hambly, John Granton

Bibliographic record

VenueExpert Review of Respiratory Medicine · 2015
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRiociguatMedicineCyclic guanosine monophosphatePulmonary hypertensionNitric oxideVasodilationPulmonary arteryInternal medicineChronic thromboembolic pulmonary hypertensionCardiologyVascular smooth muscleHemodynamicsPharmacologySmooth muscle

Abstract

fetched live from OpenAlex

Nitric oxide (NO) is a critical signaling molecule in the pulmonary vasculature. NO activates soluble guanylate cyclase (sGC) resulting in the synthesis of cyclic guanosine monophosphate (cGMP) - a key mediator of pulmonary artery vasodilatation that may also inhibit smooth muscle proliferation and platelet aggregation. Pulmonary hypertension, a serious, progressive and often fatal disease is characterized by NO-sGC-sGMP pathway dysregulation. Riociguat is a member of a novel therapeutic class known as soluble guanylate stimulators. Riociguat has a dual mode of action, acting in synergy with endogenous NO and also directly stimulating sGC independently of NO availability. Phase 3 randomized control trials have demonstrated that riociguat improves clinical, physiologic and hemodynamic parameters in patients with pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension. In this review we will discuss the pharmacologic properties of riociguat and its appropriate implementation into clinical practice.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.215
GPT teacher head0.460
Teacher spread0.245 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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