Understanding exertional dyspnea and exercise intolerance in patients with early limited cutaneous systemic sclerosis
Bibliographic record
Abstract
Background: Patients with systemic sclerosis (SSc) report exertional dyspnea and exercise intolerance (i.e., low peak oxygen uptake [V̇O2peak]). However, the underlying pathophysiology remains unclear. Aim: To characterize the dyspnea and cardiopulmonary responses to exercise in patients with early (disease duration < 5 yrs) limited cutaneous SSc. Methods: 11 patients with early limited cutaneous SSc (lcSSc; 54±11 yrs; FEV1: 97±22%predicted; DLCO: 87±15%predicted) and 7 sex and height-matched healthy controls (CON; 56±10 yrs; FEV1: 104±20%predicted; DLCO: 101±4%predicted) completed an incremental cardiopulmonary exercise test (CPET) with detailed ventilatory responses and dyspnea ratings (modified Borg Scale) obtained throughout. Results: Patients with lcSSc achieved a lower peak work rate (lcSSc: 88±23%predicted vs. CON: 127±47%predicted; P=0.02) and V̇O2peak (SSc: 83±20%predicted vs. CON: 126±41%predicted; P<0.01) compared to CON. At submaximal workloads, lcSSc patients exhibited a steeper rise in dyspnea (P=0.03), and reduced ventilatory efficiency (ventilatory equivalent to CO2 production [V̇E/V̇CO2 nadir]) than CON (lcSSc: 30±3 vs. CON: 27±4; P=0.04). Critical respiratory mechanical constraints (i.e. inspiratory reserve volume) were not different between groups (P>0.05). Conclusions: Early lcSSc is associated with elevated submaximal exertional dyspnea, reduced ventilatory efficiency, and marked exercise intolerance. These preliminary data suggest that exertional dyspnea in IcSSc may be explained by increased ventilatory drive and exercise intolerance.
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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) | 0.000 | 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 | 0.000 |
| 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".