Pulmonary Hypertension in Connective Tissue Diseases Other than Systemic Sclerosis
Bibliographic record
Abstract
Pulmonary hypertension (PH) is a known complication of certain connective tissue diseases (CTDs), with systemic sclerosis (SSc) being the most common in the Western world. However, PH in association with non-SSc CTD such as systemic lupus erythematous, mixed connective tissue disease, and primary Sjögren's syndrome constitutes a distinct subset of patients with inherently different epidemiologic profiles, pathophysiologic mechanisms, clinical features, therapeutic options, and prognostic implications. The purpose of this review is to inform a practical approach for clinicians evaluating patients with non-SSc CTD-associated PH.The development of PH in these patients involves a complex interplay between genetic factors, immune-mediated mechanisms, and endothelial cell dysfunction. Furthermore, the broad spectrum of CTD manifestations can contribute to the development of PH through various pathophysiologic mechanisms, including intrinsic pulmonary arteriolar vasculopathy (pulmonary arterial hypertension, Group 1 PH), left-heart disease (Group 2), chronic lung disease (Group 3), chronic pulmonary artery obstruction (Group 4), and unclear and/or multifactorial mechanisms (Group 5). The importance of diagnosing PH early in symptomatic patients with non-SSc CTD is highlighted, with a review of the relevant biomarkers, imaging, and diagnostic procedures required to establish a diagnosis.Therapeutic strategies for non-SSc PH associated with CTD are explored with an in-depth review of the medical, interventional, and surgical options available to these patients, emphasizing the CTD-specific considerations that guide treatment and aid in prognosis. By identifying gaps in the current literature, we offer insights into future research priorities that may prove valuable for patients with PH associated with non-SSc CTD.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".