Supine Posture Worsens Respiratory Mechanics in Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Introduction/Aim: Poor sleep quality is a major contributor to the reduced quality of life in people with chronic obstructive pulmonary disease (COPD). Supine posture is known to alter lung volume in healthy people but the effect on lung volume and respiratory mechanics in people with COPD is poorly understood. Therefore, our aim was to determine the effect of supine posture on respiratory mechanics in people with COPD. Methods: Eight participants with COPD performed baseline FOT, spirometry and lung volumes in the seated position before repeating FOT in the supine position. FOT was performed during 30s of tidal breathing in order to calculate respiratory system resistance (Rrs5) and reactance (Xrs5) at 5 Hz, as well as during a deep inspiration in order to calculate inspiratory capacity. Data are presented as mean±SD. Results: Participants were 58.3±6.2 years old with moderate airflow obstruction (FEV1 55.3±27.3% predicted), hyperinflation (FRC/TLC 126.0±20.2% predicted) and gas-trapping (RV/TLC 119±28.9% predicted). There was no effect of supine posture on inspiratory capacity (1.91±0.6 vs 2.06±0.66 L, p=0.16). However, compared to the seated position, supine posture worsened Xrs5 (-4.52±3.7 vs -5.1±4.0 cmH2O.s/L, p=0.01), while there was a trend to an increase in Rrs5 (5.31±2.19 vs 6.56±3.51 cmH2O.s/L, p=0.09). No correlation was seen between the change in Xrs5 and change in IC between postures (r=-0.12, p=0.78). However, increased worsening of Xrs5 with supine posture correlated with greater seated hyperinflation and gas trapping (r=0.75, p=0.03; r=0.84, p=0.008 respectively). Conclusion: Supine posture worsens respiratory mechanics in COPD, despite having no effect on lung volume. Greater severity of hyperinflation and gas trapping leads to worsening of airway mechanics whilst supine and this may contribute to poor sleep quality in patients with COPD.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".