Exercise Echocardiography Predicts Future Development of Pulmonary Hypertension in a High-risk Cohort of Patients with Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether a positive exercise echocardiogram (EE) predicts future development of pulmonary arterial hypertension (PAH) in a high-risk cohort of patients with systemic sclerosis (SSc). METHODS: Patients with SSc with features associated with an increased risk for PAH were recruited into a prospective, observational cohort. All patients underwent clinical assessment and EE. A positive EE was defined as an increase of ≥ 20 mmHg in the right ventricular systolic pressure with exercise. All patients with positive EE underwent right heart catheterization (RHC). RESULTS: The study included 85 patients. In the positive EE cohort, 10 of 43 patients (23%) developed resting pulmonary hypertension (PH) on RHC over a mean 4-year followup period [4 with PAH, 5 with pulmonary venous hypertension (PVH), and 1 with PH associated with interstitial lung disease]. In the persistently negative EE cohort, only 3 of 42 patients (7%) developed resting PH (1 PAH, 2 PVH; p = 0.04). Of the remaining 33 patients in the positive EE group who did not develop resting PH, 22 (67%) had a persistently positive EE over an average 5-year followup period. CONCLUSION: In this high-risk cohort of patients with SSc, a positive EE may predict the future development of resting PH. In addition, a majority of patients may have a persistently positive EE for years without progression to resting PH. Finally, a consistently negative EE may identify patients at low risk for future PH.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".