Late Breaking Abstract - An investigation of partitioned aerobic training, 1-legged cycling, on exercise endurance in patients with Idiopathic Pulmonary Fibrosis (IPF): a randomized controlled trial
Bibliographic record
Abstract
Introduction: Dyspnea due to ventilatory constraints limits effective aerobic exercise training for many patients with IPF. Ventilation per muscle load is less with 1-legged cycling (Dolmage, T.E. et al. 2020). We aimed to determine if 1-legged training (1L-TR) improves endurance more than 2-legged training (2L-TR) in patients with stable IPF enrolled in Pulmonary Rehabilitation (PR). Methods: Subjects completed conventional constant power endurance tests to intolerance (tlimit) before, midway and after being randomized to 1- or 2-legged aerobic training on a stationary cycle. Training sessions were 30 min, 3 days/wk for 6-8 wk; intensity and its progression was guided by tolerance. 1L-TR subjects switched legs mid training session. A priori sample size calculation required 20 subjects per group. Results: 40 subjects with IPF (mean±SD: FVC=62±16 %pred; GAP index=4±1; 37 on pirfenidone or nintedanib) completed the study. Both groups increased tlimit accompanied by a slower progression of heart rate. The tlimit (Figure 1) of 1L-TR was significantly greater (10.5 [3.4 to 17.6] min) than 2L-TR. erj;66/suppl_69/OA6530/F1 F1 F1 Conclusion: Partitioning training to a smaller muscle mass (1-leg) as part of PR improved endurance more than two-legged cycle training in patients with stable IPF. 1-legged cycling during PR is an option for patients with moderate-to-severe IPF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".