Inspiratory muscle weakness in mildly- to moderately-hyperinflated patients with COPD
Bibliographic record
Abstract
It is unknown whether inspiratory muscle weakness might be present and related to exertional dyspnea in patients with COPD who are not severely hyperinflated. Clinically stable patients (n=300) underwent complete pulmonary function tests and a maximal incremental cycle exercise test. After excluding patients with severe hyperinflation (IC/TLC<0.25; n=72) we compared patients with reduced (Pi,max<60%pred; n=88) and preserved (Pi,max:≥60%pred; n=140) inspiratory muscle strength (Table). Characteristics of patients with reduced (Pi,max<60%pred), and preserved (Pi,max≥60%pred) inspiratory muscle strength. Pi,max<60%pred (n=88) Pi,max≥60%pred (n=140) p-value Age (yrs) 64±9 66±9 0.045 Sex (%male) 59 57 0.785 BMI (kg/m 2 ) 27.4±5.7 29.3±6.4 0.027 Pi,max (cmH 2 O) 51±15 86±21 0.000 Pe,max (cmH 2 O) 106±37 139±47 0.000 FEV 1 (%pred) 66±21 69±23 0.270 FRC (%pred) 129±30 121±28 0.040 IC/TLC (%) 38±9 40±8 0.036 RV (%pred) 146±45 133±40 0.030 Thirty-nine percent of patients in this population had a Pi,max of less than 60% of the predicted normal value. After correcting for resting hyperinflation and other baseline differences (ANCOVA) patients with reduced Pi,max achieved lower peak work rates (W,max: 88±38 vs 76±31 Watt; p=0.004) and oxygen consumption (VO2,max: 1.5±0.6 vs 1.3±0,5 L/min; p=0.001), but higher ventilation-corrected dyspnea scores (0.105±0.052 vs 0.122±0.061; p=0.023). A higher proportion of patients with reduced Pi,max reported 'shallow breathing' at peak exercise (38% vs 20%; p=0.019). Inspiratory muscle weakness in COPD patients is prevalent even in those who are not severely hyperinflated. Inspiratory muscle weakness might contribute to dyspnea and exercise limitation in selected 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".