Patterns of dynamic IC recruitment in COPD: resting correlates and sensory-functional consequences to exercise responses
Bibliographic record
Abstract
Background: Dynamic inspiratory capacity (ICdyn) typically increases during progressive exercise in healthy individuals, allowing greater room for tidal expansion and fewer mechanical-ventilatory constraints. Some patients with COPD might be able to increase ICdyn despite expiratory flow limitation (IC recruiters). Aim: To contrast resting lung function and exercise parameters germane to exertional dyspnoea in ICdyn recruiters and non-recruiters. Methods: 352 patients (194 ♂, aged 41-86yrs, FEV1 17-124 % predicted) underwent incremental CPET. ICdyn recruitment was defined as an increase in IC compared to rest >150mL or 4.5% pred at the highest equivalent work rate of 60 W (Figure 1A). Results: Recruiters composed ~30% of the sample. Compared with non-recruiters, they were leaner and less obstructed (⇑ FEV1 and FEV1/FVC), showing ⇓ IC but similar operating lung volumes at rest (Figure 1B). ICdyn recruitment consistently lowered mechanical-ventilatory constraints throughout exercise and dyspnoea at 60W (Figure 1B-1F). Peak Borg dyspnoea≥leg discomfort scores were found in ~1/3 of recruiters and ~2/3 of non-recruiters. The former group also showed higher peak O2 uptake (86±45 vs 76±37 % pred; p<0.05). erj;66/suppl_69/OA5402/F1 F1 F1 Conclusions: ICdyn recruitment at submaximal exercise lessens mechanical-ventilatory constraints, contributing to lower exertional dyspnoea and higher exercise tolerance in a subset of COPD 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".