The contribution of FEV <sub>1</sub> and airflow limitation on the intensity of dyspnea and leg effort during exercise. Insights from a real‐world cohort
Bibliographic record
Abstract
Rationale The effort required to cycle and breathe intensify as power increases during incremental exercise. It is currently unclear how changes in FEV1 in the presence or absence of airflow limitation) impacts the intensity of dyspnea and leg effort. This is clinically important as the improvement in FEV1 is often the target for improving dyspnea. Objectives To investigate the relationship between dyspnea (D), leg effort, power (P), and FEV1 with and without airflow limitation using direct psychophysical scaling performed during incremental exercise testing to symptom limited capacity. Methods Retrospective analysis of consecutive patients over the age of 35 referred for cardio-pulmonary exercise testing at McMaster University Medical Centre from 1988–2012.The modified Borg scale was used to measure dyspnea throughout incremental exercise testing. Measurements and results 38,788 patients were included in the analysis [Mean Age 58.6 years (SD ±11.8), Males 61%, BMI 28.1 kg/m2 (SD ±5.1), FEV1 was 2.7 L (SD ±0.85), 95% predicted (SD ±20.4), FVC 3.4 L (SD ± 1.0), 94% predicted (SD ±17.0)], and 10.9% had airflow limitation (AL, FEV1/FVC < 70%). In a nonlinear regression analysis, the intensity of dyspnea increased in a positively accelerating manner with power and as the FEV1% predicted decreased: Dyspnea = 0.06 * Power1.03 * FEV1%Pred−0.66(r = .63). The intensity of leg effort increased with power and declining quadricep strength and FEV1% predicted: Leg Effort = 0.06 * Power1.22 * Quad−0.56*FEV1%Pred−0.39(r = .73). There was no independent effect of AL on dyspnea of leg effort. Conclusion Power, quadriceps strength and FEV1 are the dominant factors contributing to dyspnea and leg effort, irrespective of the degree of airflow limitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".