Is the Lung Built for Exercise? Advances and Unresolved Questions
Bibliographic record
Abstract
ABSTRACT: Nearly 40 yr ago, Professor Dempsey delivered the 1985 ACSM Joseph B. Wolffe Memorial Lecture titled: "Is the lung built for exercise?" Since then, much experimental work has been directed at enhancing our understanding of the functional capacity of the respiratory system by applying complex methodologies to the study of exercise. This review summarizes a symposium entitled: "Revisiting 'Is the lung built for exercise?'" presented at the 2022 American College of Sports Medicine annual meeting, highlighting the progress made in the last three-plus decades and acknowledging new research questions that have arisen. We have chosen to subdivide our topic into four areas of active study: (i) the adaptability of lung structure to exercise training, (ii) the utilization of airway imaging to better understand how airway anatomy relates to exercising lung mechanics, (iii) measurement techniques of pulmonary gas exchange and their importance, and (iv) the interactions of the respiratory and cardiovascular system during exercise. Each of the four sections highlights gaps in our knowledge of the exercising lung. Addressing these areas that would benefit from further study will help us comprehend the intricacies of the lung that allow it to meet and adapt to the acute and chronic demands of exercise in health, aging, and disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".