Treatment of pulmonary hypertension with selective pulmonary vasodilators
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pulmonary vasodilators are important in the management of pulmonary hypertension. Although systemic vasodilators may be effective in lowering pulmonary artery pressure, systemic vasodilation is the main limitation to dose titration. This review summarizes the latest research and developments in pulmonary vasodilators in the management of acute and chronic pulmonary hypertension. RECENT FINDINGS: Nitric oxide, the prototype of selective pulmonary vasodilators, remains an effective option in the management of pulmonary hypertension; however, cost and complexity of administration have led to consideration of other pulmonary vasodilators. Animal research suggests that nitric oxide may have an important role in the prevention of pulmonary hypertension after cardiopulmonary bypass. Experience with phosphodiesterase inhibitors, as monotherapy or as part of combination therapy, suggests that these agents improve cardiopulmonary hemodynamics and can be considered as alternatives and/or adjuncts to nitric oxide. Prostacyclins are a versatile class of pulmonary vasodilator as they have been shown to improve pulmonary hemodynamics administered intravenously or via inhalation. Endothelin receptor antagonists have been shown to be effective for long-term management of pulmonary hypertension. Several gene therapy strategies are currently undergoing evaluation. SUMMARY: Selective pulmonary vasodilation can be achieved through delivery of vasodilators directly to the lungs or targeting pulmonary specific processes. Several therapeutic options are available that demonstrate selectivity for the pulmonary vasculature. These agents can facilitate optimization of cardiopulmonary hemodynamics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".