Influence of conical-PEP breathing on exercise performance in patients with chronic obstructive pulmonary disease: A single-blind randomized crossover trial
Bibliographic record
Abstract
Background Conical-positive expiratory pressure (conical-PEP) has been applied during exercise to improve exercise capacity, dynamic hyperinflation (DH), and dyspnea in COPD. However, evidence remains limited regarding the individualized selection of its resistor (orifice size) and its effects on exercise duration, DH development, and dyspnea. Method A randomized crossover trial was conducted to evaluate the effects of conical-PEP. Participants performed spot marching exercise while breathing through a conical-PEP device with a mask, compared to a sham-PEP condition in which a similar mask was worn without the conical-PEP component. The conical-PEP resistor was selected to achieve the minimum required PEP level, calculated by the proposed formula, while ensuring that pressure did not exceed 35 cmH₂O. Exercise endurance time, end-exercise inspiratory capacity (IC) to assess DH, and dyspnea using the modified Borg scale were recorded. Results Twenty moderate to severe COPD subjects (19 male, 1 female, age 67.40 ± 8.22 years, FEV1% predicted 56.05 ± 16.90) participated. Conical-PEP resulted in longer exercise time (4.98 ± 2.97 minutes) than sham-PEP (3.99 ± 2.19 minutes, p = 0.004). End-exercise IC was significantly better in conical-PEP (1.51 [1.24, 1.85] L) than sham-PEP (1.42 [1.16, 1.84] L, p = 0.020). Dyspnea was significantly lower in conical-PEP at iso-time (4 [4, 5]) compared to sham-PEP (5 [5, 6], p = 0.005), though no significant difference was found at end exercise. Conclusion Conical-PEP with minimum required PEP level improves exercise capacity, delays DH development, and delays dyspnea onset in 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".