Synergetic interactions between rehabilitation and pharmacotherapy in COPD.
Bibliographic record
Abstract
BACKGROUND: Once viewed as an irreversible condition, chronic obstructive pulmonary disease (COPD) is now considered as a preventable and treatable disease. The past ten years of research have clearly indicate that dyspnea, exercise tolerance and quality of life can be improved considerably with appropriate therapeutic interventions that include pharmacological and non-pharmacological components. It is also becoming evident that it is the concomitant use of appropriate pharmacotherapy and non-pharmacological approaches, such as exercise training and pulmonary rehabilitation, that offers the best hope for an optimal status. PURPOSE: The objective of this short paper is to review the rationale of combining pharmacological and non-pharmacological therapeutic approaches to optimize functional status and quality of life in patients with COPD. PRINCIPAL FINDINGS: Optimal bronchodilation is the mainstay of treatment. Leg fatigue will prevent patients with COPD from obtaining full advantage of bronchodilation. Quadriceps fatigue during cycling exercise is linked to events taking place within the muscle providing a muscular and metabolic basis to explain the observation that some patients with COPD develop contractile fatigue after exercise. Muscle fatigue can be improved with exercise training. Pharmacotherapy and exercise training offer the best hope for an optimal status in COPD. CONCLUSION: Our goals are to stimulate interest in COPD, provide a strong case against the nihilistic approach to this disease to ultimately raise the standard of care to the benefit of the numerous patients afflicted by this condition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".