Pharmacological management of connective tissue disease-associated pulmonary arterial hypertension
Bibliographic record
Abstract
INTRODUCTION: Pulmonary arterial hypertension (PAH) is a severe, progressive pulmonary vasculopathy (Group 1 Pulmonary Hypertension (PH)) that complicates the course of many connective tissue diseases (CTD). Detailed testing is required to differentiate PAH from other types of PH caused by CTD such as left heart disease (Group 2 PH), pulmonary parenchymal disease (Group 3 PH), and chronic thromboembolic pulmonary hypertension (Group 4 PH). PAH is most frequently seen in systemic sclerosis but can also be seen with systemic lupus erythematosus, mixed CTD, and primary Sjogren's syndrome. AREAS COVERED: This review discusses the epidemiology of CTD-associated PAH, outlines the complex diagnosis approach, and finishes with an in-depth discussion on the current treatment paradigm. Focus is placed on challenges faced in the treatment of CTD-associated PAH, (decreased efficacy and poorer tolerance of pharmacological therapies) and includes a discussion on the future investigational treatments. EXPERT OPINION: Despite significant advances over the past decades with more aggressive treatment algorithms, CTD-associated PAH patients continue to have poorer survival compared to those with idiopathic PAH. This review highlights factors leading to disparate outcomes compared to other forms of PAH, and discusses on further improvements that may increase quality of life and survival for CTD-associated PAH patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".