Objective criteria to characterize an erratic breathing pattern in dyspnoeic patients undergoing cardiopulmonary exercise testing
Bibliographic record
Abstract
Dysfunctional breathing (DB) is a common, but underappreciated, cause of dyspnoea. Subjects suffering from DB characteristically show a large variability on resting and/or exertional breathing pattern plus excessive ventilatory response to metabolic demand. Currently, there are no established criteria to objectively determine the presence of DB during incremental cardiopulmonary exercise testing (CPET). Patients with chronic dyspnoea who were considered as having DB using a gestalt/pattern recognition approach during cycling CPET (N=20,5♂,55.7±14.4 yrs) were compared with matched controls (N=10,3♂,50.7±13.2 yrs). Ventilatory responses were obtained on a breath-by-breath basis and averaged as arithmetic means of 20s. Amplitudes of breathing pattern changes (Δ=higher 20s-lower 20s values) at rest, 2sd min of unloaded cycling, and 3rd min of loaded exercise were compared between groups. Patients presented with ⇓ peak O2 uptake(81±27 vs 111±22%pred) and ⇑ ventilation(V̇E)-carbon dioxide output slope (35.7±4.2 vs 29.6±4.6L/L) and dyspnoea severity compared to controls. From all breathing pattern parameters, only Δ breathing frequency(f) and Δf/tidal volume (VT) at rest, and Δf, Δf/VT, and ΔV̇E at 3rd min of loaded exercise were significantly higher in patients (p<0.05). The optimal cutoffs values are shown below: This is the first frame of reference to help the practitioner to judge the presence of an erratic breathing pattern during incremental CPET in dyspnoeic patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".