Reference equations for breathlessness during incremental cycle exercise testing
Bibliographic record
Abstract
Background Exertional breathlessness is commonly assessed using incremental exercise testing (IET), but reference equations for breathlessness responses are lacking. We aimed to develop reference equations for breathlessness intensity during IET. Methods A retrospective, consecutive cohort study of adults undergoing IET was carried out in Sweden. Exclusion criteria included cardiac or respiratory disease, death or any of the aforementioned diagnoses within 1 year of the IET, morbid obesity, abnormally low exercise capacity, submaximal exertion or an abnormal exercise test. Probabilities for breathlessness intensity ratings (Borg CR10) during IET in relation to power output (%predWmax), age, sex, height and body mass were analysed using marginal ordinal logistic regression. Reference equations for males and females were derived to predict the upper limit of normal (ULN) and the probability of different Borg CR10 intensity ratings. Results 2581 participants (43% female) aged 18–90 years were included. Mean breathlessness intensity was similar between sexes at peak exertion (6.7±1.5versus6.4±1.5 Borg CR10 units) and throughout exercise in relation to %predWmax. Final reference equations included age, height and %predWmaxfor males, whereas height was not included for females. The models showed a close fit to observed breathlessness intensity ratings across %predWmaxvalues. Models using absolute W did not show superior fit. Scripts are provided for calculating the probability for different breathlessness intensity ratings and the ULN by %predWmaxthroughout IET. Conclusion We present the first reference equations for interpreting breathlessness intensity during incremental cycle exercise testing in males and females aged 18–90 years.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".