Cardiopulmonary exercise testing to indicate increased ventilatory variability in subjects with dysfunctional breathing
Bibliographic record
Abstract
Abstract Background Dysfunctional breathing (DB) is a common, but largely underappreciated, cause of chronic dyspnoea. Under visual inspection, most subjects with DB present with larger sequential changes in ventilation (V̇E) and breathing pattern (tidal volume (VT) and breathing frequency (f)) before and/or during incremental cardiopulmonary exercise testing (CPET). Currently, however, there are no objective criteria to indicate increased ventilatory variability in these subjects. Methods Twenty chronically dyspnoeic subjects with DB and 10 age‐ and sex‐matched controls performed CPET on a cycle ergometer. Cut‐offs to indicate increased V̇E, VT, f, and f/VT ratio variability (Δ = highest‐lowest 20 s arithmetic mean) over the last resting minute (rest), the 2sd min of unloaded exercise (unload), and the 3rd min of loaded exercise (load) were established by ROC curve analyses. Results Subjects with DB presented with increased V̇E, higher ventilatory variability, higher dyspnoea burden, and lower exercise capacity compared to controls (p < 0.05). ΔV̇Eload (>4.1 L/min), Δfrest (>5 breaths/min; bpm), Δfunload (>4 bpm), Δfload (>5 bpm), Δf/VTrest (>4.9 bpm/L), and Δf/VTload (>1.3 bpm/L) differentiated DB from a normal pattern (areas under the curve ranging from 0.729 to 0.845). High Δf, in particular, was associated with DB across all CPET phases. Conclusions This study provides objective criteria to indicate increased ventilatory variability during incremental CPET in dyspnoeic subjects with DB. Large variability in breathing frequency seems particularly useful in this context, a finding that should be prospectively confirmed in larger studies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".