Impact of single-limb (SL) versus two-limb (TL) low load/high-repetition resistance training (LLHR-RT) on clinical outcomes in people with COPD – a randomized controlled trial.
Bibliographic record
Abstract
Thirty-two participants with COPD (FEV1:38±10% predicted) were randomized to either 8-weeks of SL or TL LLHR-RT. LLHR-RT was performed three times/week for 8 weeks and consisted of knee extension (KE), leg curl (LC), latissimus row (ROW), chest press (CP), elbow flexion (EF) shoulder flexion (SF), and calf-raise exercises. Outcomes: 6MWT (primary outcome), unsupported upper limb exercise test (UULEX), COPD assessment test (CAT), isotonic muscle endurance (KE, LC, ROW, CP, EF, SF), exercise workloads and dyspnea. In addition, exertional symptoms were assessed at two exercise sessions with matched workloads that were performed before and post intervention. Intention to treat analysis was performed using multiple imputation. Results: During the 8-week intervention period, exercise workloads were similar, mean difference per week SL versus TL: (56 [-47 to 161 kg]) but dyspnea ratings were lower (-1.4 [- 0.1 to - 2.8]) during SL. Impact of LLHR-RT on clinical outcomes are shown in Table. *p<0.001, Ɨ number of repetitions * loading (kg) across exercises Conclusion: SL and TL LLHR-RT results in similarly positive effects but with lower level of exertional dyspnea during the former.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".