Pulmonary Hypertension Assessment and Recognition of Outcomes in Scleroderma (PHAROS): Baseline Characteristics and Description of Study Population
Bibliographic record
Abstract
OBJECTIVE: Pulmonary arterial hypertension (PAH) increases mortality in systemic sclerosis (SSc). The multicenter PHAROS registry (Pulmonary Hypertension Assessment and Recognition of Outcomes in Scleroderma) prospectively follows subjects with SSc at high risk for or with incident pulmonary hypertension (PH). We describe the registry design and baseline characteristics of subjects enrolled during the first 18 months since the start of the study. METHODS: High-risk subjects are enrolled and classified as Pre-PAH if they have (1) carbon monoxide diffusing capacity (DLCO) < 55% predicted; (2) percentage of predicted forced vital capacity/DLCO ratio ≥ 1.6; or (3) an estimated right ventricular systolic pressure > 35 mm Hg on echocardiography. Subjects with right heart catheterization (RHC)-confirmed incident PH (mean pulmonary artery pressure ≥ 25 mm Hg within previous 6 months) are subclassified into PAH, pulmonary venous hypertension secondary to left-side heart disease (PVH), and PH due to interstitial lung disease (PH-ILD). Baseline and biannual demographic, clinical, and laboratory data and patient-reported health questionnaires are collected. RESULTS: There are 237 subjects enrolled in PHAROS. The majority are white (73%) and women (87%). There are 166 Pre-PAH and 71 Definite PH subjects (49 PAH, 7 PVH, and 15 PH-ILD). CONCLUSION: PHAROS is the largest US and Canadian cohort of subjects with SSc at high risk for or with incident PAH. PAH-specific therapies are approved for 49/71 subjects with RHC-confirmed PAH. Analyses of PHAROS registry data will permit identification of risk factors for development of PAH among SSc patients at high risk for PAH and enhance understanding of the course of SSc-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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".