Screening for pulmonary arterial hypertension in systemic sclerosis
Bibliographic record
Abstract
Pulmonary arterial hypertension (PAH) is a dreaded complication of systemic sclerosis (SSc) that occurs in ∼10% of patients. Most individuals present with severe symptoms, significant functional impairment and severe haemodynamics at diagnosis, and survival after PAH diagnosis is poor. Therefore, early diagnosis through systematic screening of asymptomatic patients has the potential to identify PAH at an early stage. Current evidence suggests that early diagnosis and treatment of PAH in patients with SSc may lead to better clinical outcomes. Annual screening may include echocardiography, but this can miss some patients due to suboptimal visualisation or insufficient tricuspid regurgitation. Other options for screening include the DETECT algorithm or the use of a combination of pulmonary function testing (forced vital capacity/diffusing capacity of the lung for carbon monoxide ratio) and N-terminal-pro-brain natriuretic peptide levels. Symptomatic patients, those with an elevated tricuspid regurgitation velocity on echocardiogram with or without secondary echocardiographic features of PAH, and those who screen positive on the DETECT or other pulmonary function test algorithms should undergo right heart catheterisation. Exercise echocardiography or cardiopulmonary exercise testing, nailfold capillaroscopy and molecular biomarkers are promising but, as yet, unproven potential options. Future screening studies should employ systematic catheterisation to define the true predictive values for PAH.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".