Limited Exercise Capacity in Patients with Systemic Sclerosis: Identifying Contributing Factors with Cardiopulmonary Exercise Testing
Bibliographic record
Abstract
Objective. Exercise limitation in patients with systemic sclerosis (SSc) is often multifactorial and related to complications such as interstitial lung disease (ILD), pulmonary vasculopathy (PV), left ventricular dysfunction (LVD), and/or peripheral/muscular limitation (PML). We hypothesized that cardiopulmonary exercise testing (CPET) could not only suggest and rank competing etiologies, but also highlight peripheral impairment. Methods. Clinical, resting pulmonary function testing, and CPET data from patients with SSc referred for exercise limitation between October 2009 and November 2015 were retrospectively analyzed in this bi-center study. Patients were categorized as having ILD, PV, LVD, and/or PML based on CPET response patterns and the diagnoses were matched with results from the reference investigations. The latter consisted of transthoracic echocardiography, chest computed tomography scan, and right heart catheterization (RHC). Results. Twenty-seven patients presented with CPET profiles consistent with ILD (n = 16), PV (n = 15), LVD (n = 5), and PML (n = 19). None of the subjects had a normal CPET profile. There was a statistically significant negative correlation between resting DLCO, on the one hand, and dead space to tidal volume ratio and alveolar–arterial gradient [P(Ai-a)O2] on the other (p < 0.005). CPET identified 90% of patients with a mean pulmonary arterial pressure at rest ≥ 21 mmHg measured by RHC (n = 10). Peak P(Ai-a)O2, taken independently from other variables, was crucial in distinguishing subjects with ILD from those without ILD (p < 0.05). Conclusion. CPET is useful for the characterization of multifactorial exercise limitation in patients with SSc and in identifying SSc-related complications such as ILD and PV. This study also identifies PML as an underestimated cause of exercise limitation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".