Management of Pulmonary Arterial Hypertension
Bibliographic record
Abstract
Pulmonary arterial hypertension (PAH) is a rare pulmonary vascular disease characterized by progressive pulmonary arterial remodeling, increased pulmonary vascular resistance, right ventricular dysfunction, and reduced survival. Effective therapies have been developed that target three pathobiologic pathways in PAH: nitric oxide, endothelin-1, and prostacyclin. Approved therapies for PAH include phosphodiesterase type-5 inhibitors, soluble guanylate cyclase stimulators, endothelin receptor antagonists, prostacyclin analogs, and prostacyclin receptor agonists. Management of PAH in the modern era incorporates multidimensional risk assessment to guide the use of these medications. For patients with PAH and without significant comorbidities, current guidelines recommend two oral medications (phosphodiesterase type-5 inhibitor and endothelin receptor antagonist) for low- and intermediate-risk patients, with triple therapy including a parenteral prostacyclin to be considered in those at high or intermediate-high risk. Combination therapy may be poorly tolerated and less effective in patients with PAH and cardiopulmonary comorbidities. Thus, a single-agent approach with individualized decisions to add-on other PAH therapies is recommended in older patients and those with significant comorbid conditions. Management of PAH is best performed in multidisciplinary teams located in experienced centers. Other core pillars of PAH management include supportive and adjunctive treatments including oxygen, diuretics, rehabilitation, and anticoagulation in certain patients. Patients with PAH who progress despite optimal treatment or who are refractory to best medical care should be referred for lung transplantation, if eligible. Despite considerable progress, PAH is often fatal and new therapies that reverse the disease and improve outcomes are desperately needed.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".