Resting lung volumes in mild to severe fibrosing ILD: implications for dyspnoea and exercise tolerance
Bibliographic record
Abstract
Background: Exertional dyspnoea is influenced by the severity of restriction (⇓ total lung capacity (TLC)) in fibrosing ILD (f-ILD). Compensatory decrements in residual volume (RV) and functional residual capacity (FRC) may relatively preserve (⇔) the limits for tidal expansion, mitigating dyspnoea despite ⇓ TLC. Aim: To contrast sensory-physiological responses to exercise in restricted and non-restricted f-ILD showing ⇔ or ⇓ inspiratory capacity (IC). Methods: 95 patients (68 men, TLC from -5.05 to 1.03 z-score) completed an incremental CPET. A novel AI-based software (DyVe-X) quantified the severity of mechanical-ventilatory constraints and exertional dyspnoea. Resting lung volumes were compared with GLI standards. Results: All but one non-restricted patients had ⇔ IC. ~ 50 % of restricted patients had ⇓ IC; they showed the lowest RV/TLC and FRC/TLC, i.e., the highest vital capacity/TLC and IC/TLC (Figure 1A). Despite ~ 1 L lower TLC than non-restricted patients, they had similar exercise capacity (peak work rate= 75.1 ± 27.3 vs. 72.8 ± 28.1 %, respectively; p>0.05). This finding was associated with consistently higher mechanical-ventilatory reserves (Figure 1B-1C) and lower dyspnoea (Figure 1D) than restricted patients with ⇓ IC (p<0.05). erj;66/suppl_69/PA2043/F1 F1 F1 Conclusions: IC should be considered in conjunction with TLC to estimate the burden of activity-related dyspnoea and exercise intolerance in restricted patients with f-ILD
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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 | 0.001 |
| 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".