Identifying Abnormal Exertional Breathlessness in COPD
Bibliographic record
Abstract
BACKGROUND: COPD management is guided by the respiratory symptom burden, assessed using the modified Medical Research Council (mMRC) scale, the COPD Assessment Test (CAT), or both. RESEARCH QUESTION: What are the abilities of mMRC and CAT to detect abnormally high exertional breathlessness on incremental cardiopulmonary cycle exercise testing (CPET) in people with COPD? STUDY DESIGN AND METHODS: to FVC ratio of < 0.70 after bronchodilator administration and ≥ 10 pack-years of smoking from the Canadian Cohort Obstructive Lung Disease study. Abnormal exertional breathlessness was defined as a breathlessness (Borg scale 0-10) intensity rating more than the upper limit of normal at the symptom-limited peak of CPET using normative reference equations. RESULTS: of 79.5% predicted (19.0% predicted); 26% showed abnormally low exercise capacity (peak oxygen uptake less than the lower limit of normal). Abnormally high exertional breathlessness was present in 24%, including 9% and 11% of people with mMRC score of 0 and CAT score of < 10, respectively. An mMRC score of ≥ 2 and CAT score of ≥ 10 was most specific (95%) to detect abnormal exertional breathlessness, but showed low sensitivity of only 12%. Accuracy for all scale cutoffs or combinations was < 65%. Compared with people with true-negatives findings, people with abnormal exertional breathlessness but low mMRC score, low CAT scores (false-negatives findings), or both showed worse self-reported and physiologic outcomes during CPET, were more likely to have physician-diagnosed COPD, but were not more likely to be taking any respiratory medication (37% vs 30%; mean difference, 6.1%; 95% CI, -7.2 to 19.4; P= .36). INTERPRETATION: In COPD, mMRC and CAT showed low concordance with CPET and failed to identify many people with abnormally high exertional breathlessness. CLINICAL TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT00920348; URL: www. CLINICALTRIALS: gov.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".