Clinical Utility of Measuring Inspiratory Neural Drive During Cardiopulmonary Exercise Testing (CPET)
Bibliographic record
Abstract
Cardiopulmonary exercise testing (CPET) has traditionally included ventilatory and metabolic measurements alongside electrocardiographic characterization; however, research increasingly acknowledges the utility of also measuring inspiratory neural drive (IND) through its surrogate measure of diaphragmatic electromyography (EMGdi). While true IND also encompasses the activation of non-diaphragmatic respiratory muscles, the current review focuses on diaphragmatic measurements, providing information about additional inspiratory muscle groups for context where appropriate. Evaluation of IND provides mechanistic insight into the origins of dyspnea and exercise limitation across pathologies; yields valuable information reflecting the integration of diverse mechanical, chemical, locomotor, and metabolic afferent signals; and can help assess the efficacy of therapeutic interventions. Further, IND measurement during the physiologic stress of exercise is uniquely poised to reveal the underpinnings of physiologic limitations masked during resting and unloaded breathing, with important information provided not only at peak exercise, but throughout exercise protocols. As our understanding of IND presentation across varying conditions continues to grow and methods for its measurement become more accessible, the translation of these principles into clinical settings is a logical next step in facilitating appropriate and nuanced management tailored to each individual's unique physiology. This review provides an overview of the current state of understanding of IND measurement during CPET: its origins, known patterns of behavior and links with dyspnea in health and major respiratory diseases, and the possibility of expanding this approach to applications beyond exercise.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".