Neural or muscular adaptations to low-load/high-repetition knee extension training in people with COPD
Bibliographic record
Abstract
During the course of a typical strength training program, neural factors account for approx. 90% of gain in strength during the first 2-weeks and 40-50% during the subsequent 2-weeks while muscular adaptations become progressively more important. We aimed to investigate the impact of the rate of progression of muscle work performed during training sessions on intramuscular adaptations in people with COPD following resistance training designed to improve quadriceps muscle endurance. Fourteen participants with COPD (age: 65±7 years, FEV1 :39±10 % predicted) performed a muscle biopsy before and after an 8-week training intervention consisting of low-load/high-repetition knee extension exercise. Those in whom the gain in muscle endurance occurred the first 4 weeks were considered as the "neural" group while those who showed continuous progression throughout the whole 8-week period constituted the "muscular" group. Results are seen in Figure 1. Conclusion: Eight weeks of low-load/high-repetition resistance training was associated with an increase in the proportion of type I fibers with a recriprocal decrease in percentage of Type IIa fibers among patients with COPD with a continuous progression in knee extension work capacity throughout an 8-week training intervention in comparison to those in whom the gain in muscle endurance occurred the first 4 weeks of training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".