When to Best Assess Breathlessness Abnormality During Incremental Cardiopulmonary Cycle Exercise Testing
Bibliographic record
Abstract
BACKGROUND: Breathlessness on exertion is a common, distressing, and limiting symptom that can be quantified on incremental cardiopulmonary exercise testing (CPET) using normative reference equations. RESEARCH QUESTION: Is the breathlessness abnormality best uncovered and assessed at symptom limitation (peak exercise) compared with submaximal exercise intensities? STUDY DESIGN AND METHODS: This was an analysis of people ≥ 40 years of age undergoing symptom-limited incremental cycle CPET in the Canadian Cohort Obstructive Lung Disease (CanCOLD) study. Each Borg 0-10 category ratio scale breathlessness intensity rating during CPET was converted to its probability of being normal, in relation to power output, rate of oxygen uptake, and minute ventilation using normative reference equations. Abnormally high exertional breathlessness (abnormal breathlessness) was defined as a probability of being normal < 0.05. RESULTS: Of 1,161 participants (42% female), abnormally high breathlessness was present in 22%, 23%, and 16% in relation to rate of oxygen uptake and minute ventilation at peak exercise. Among those with abnormal breathlessness at peak exercise, 55% to 60% had normal breathlessness across all submaximal exercise intensities. Among those with normal breathlessness at peak exercise, 93% to 97% were normal across all serial breathlessness ratings throughout the CPET (interclass correlation coefficients, 0.93-0.95). Findings were similar in people with or without chronic airflow limitation, and in people who did or did not reach maximal exertion at the end (symptom limitation) of the CPET. INTERPRETATION: The results of this study suggest that abnormal breathlessness is uncovered and should be assessed at peak exercise during symptom-limited incremental CPET. These findings inform symptom assessment in research and clinical practice.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".