Quantifying exertional symptoms throughout incremental CPET adds value to peak measurements across the range of COPD severity
Bibliographic record
Abstract
Background: Isolated measurements of dyspnoea and leg effort at peak exercise may provide an imperfect picture of the actual symptom burden in patients with COPD. Aim: To investigate whether assessing submaximal symptom burden across progressively higher exercise intensities adds value to discrete peak symptom scores. Methods: After developing sex- and age-adjusted standards for CR 10 Borg dyspnoea and leg effort versus work rate, we used a novel AI-based software (DyVe-X) to determine the overall burden of each symptom (best centile of intensity) during incremental cycle ergometry in 354 patients (196 men) with COPD of varied severity. Results: Leg effort was the main limiting symptom in 158/354 patients (44.6%); 88 of them (55.7%) showed “severe-to-very severe” (>75th centile) submaximal dyspnoea. Conversely, dyspnoea was the main limiting symptom in 99/354 patients (28.0%): 45 of them (64.18%) showed “severe-to-very severe” submaximal leg effort (p<0.01) (upper panel). Moreover, peak leg effort and dyspnoea typically underestimated their individual submaximal burden (lower panel) across GOLD stages (p<0.01). erj;64/suppl_68/OA1955/F1 F1 F1 Conclusions: Peak scores of dyspnoea and leg effort underestimated the severity of each symptom as measured throughout incremental exercise in COPD. A novel AI-based approach better exposed the actual extension of the overall symptom burden experienced by these patients.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".