Normative Reference Equations for Breathlessness Intensity During Incremental Cardiopulmonary Cycle Exercise Testing
Bibliographic record
Abstract
Abstract Rationale Cardiopulmonary exercise testing (CPET) is the gold standard to evaluate exertional breathlessness, a common and disabling symptom. However, the interpretation of breathlessness responses to CPET is limited by a scarcity of normative data. Objectives We aimed to develop normative reference equations for breathlessness intensity (Borg 0–10 category ratio) response in men and women aged ⩾40 years during CPET, in relation to power output (watts), oxygen uptake, and minute ventilation. Methods Analysis of ostensibly healthy people aged ⩾40 years undergoing symptom-limited incremental cycle CPET (10 W/min) in the CanCOLD (Canadian Cohort Obstructive Lung Disease) study. Participants had smoking histories <5 pack-years and normal lung function and exercise capacity. The probability of each Borg 0–10 category ratio breathlessness intensity rating by power output, oxygen uptake, and minute ventilation (as an absolute or a relative value [percentage of predicted maximum]) was predicted using ordinal multinomial logistic regression. Model performance was evaluated by fit, calibration, and discrimination (C statistic) and externally validated in an independent sample (n = 86) of healthy Canadian adults. Results We included 156 participants (43% women) from CanCOLD; the mean age was 65 (range, 42–91) years, and the mean body mass index was 26.3 (standard deviation, 3.8) kg/m2. Reference equations were developed for women and men separately, accounting for age and/or body mass. Model performance was high across all equations, including in the validation sample (C statistic for men = 0.81–0.92, C statistic for women = 0.81–0.96). Conclusions Normative reference equations are provided to compare exertional breathlessness intensity ratings among individuals or groups and to identify and quantify abnormal breathlessness responses (scores greater than the upper limit of normal) during CPET.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 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".