Effect of respiratory biofeedback on effort dyspnea in COPD patients during pulmonary rehabilitation: a pilot study
Bibliographic record
Abstract
Background: Patients with advanced Chronic Obstructive Pulmonary Disease (COPD) experience effort dyspnea despite maximal drug therapy. Respiratory BF (R-BF) uses sensors for monitoring of electrophysiological parameters and consisted in the patient's progressive education in slow and deep breathing (1). We aim to assess if R-BF training in addition to pulmonary rehabilitation (PR) in patients with optimal pharmacological therapy may determine a benefit on of effort dyspnea. Methods: 17 stable COPD patients (all E group) with modified Medical Research Council(mMRC)>3 despite maximal pharmacological therapy were consecutively enrolled for 6 months and randomly divided into two groups: group 1, intervention group(PR+BF); group 2, control group(only PR). The study outcomes were: mMRC, BORG dyspnea, BARTHEL dyspnea, SPPB, 6-minute walk test distance (6MWD), St. George Respiratory Questionnaire (SGRQ), EuroQOL-5 Dimension Questionnaire (EQ-5D™), Montreal Cognitive Assessment (MoCA Test), The Hospital Anxiety and Depression Scale (HADS A and D) Results: after rehabilitation, mMRC, BORG D, BARTHEL dyspnea and quality of life questionnarire increased in both groups. In addition, only group 1 showed improvement in 6MWD(p=0.041) and in cognitive domains(p=0.006), specifically in:Executive Functions(p=0.015), Memory(p=0.019), Language(p=0.045), Orientation(p=0.046) Conclusions: R-BF does not provide additional benefit on dyspnea, but in combination with PR may promote additional benefits not only on motor but also cognitive outcomes in COPD patients with effort dyspnea despite optimal therapy Ref: 1)de Souto Barbosa JV Appl Psychophysiol Biofeedback.2023Dec;48(4):423-432
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".