A human model of the pathophysiology of chronic obstructive pulmonary disease
Bibliographic record
Abstract
This short review summarizes a series of studies on the effects of expiratory flow limitation (EFL) at approximately 1 L/s during incremental exercise to maximal workload (Wmax) in normal subjects on exercise performance, respiratory muscle dynamics and control, and CO(2) elimination. Each subject served as his or her own control by performing the same protocol without EFL. Additionally, an index of cardiac output was measured before and after imposing EFL while the subjects exercised at Wmax, Wmax was reduced to 65% of control by severe dyspnoea. EFL forced a decrease in the shortening velocity of expiratory muscles, resulting in increased expiratory pressures which accounted for 66% of the variance in Borg scale ratings of dyspnoea. In spite of an increase in the shortening velocity of inspiratory muscles, inspiratory pressures and power increased, because EFL exercise induced hypercapnia, which increased the chemical drive to breathe. This was in part due to an increased alveolar dead space presumably resulting from a reduction in pulmonary capillary blood volume secondary to the high expiratory pressures. A vicious circle was established in which expiratory muscle pressures induced hypercapnia, which resulted in an even stronger expiratory muscle contraction. The imposition of EFL reduced cardiac output by 10% and decreased arterial O(2) saturation, reducing energy supplies to working locomotor and respiratory muscles. This model reproduces the most important clinical features of COPD, and these arise from ventilatory pump dysfunction rather than from the lung. It also leads to hypotheses that can be tested in patients with COPD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".