Physiotherapy Education and Breathing Retraining for Patients With Hyperventilation Syndrome: An Observational Study
Bibliographic record
Abstract
Purpose: The hyperventilation syndrome (HVS) is a dysfunctional breathing disorder with significant consequences on quality of life. HVS treatment involves an educational approach and breathing exercises. Although better understanding of HVS is needed, it is also relevant to provide evidence of HVS physiotherapy treatment efficiency. This study aimed to assess the effects of respiratory physiotherapy in HVS patients. Method: Patients referred by a physician to a physiotherapist for HVS-related symptoms were included in this prospective observational study. They received six respiratory physiotherapy sessions (1/wk) composed of therapeutic education and breathing exercises. The primary objective was to assess the pre-post change on HVS symptoms, using the Nijmegen Questionnaire, between baseline and 5 weeks. Secondary objectives assessed anxiety, depression, dyspnoea, quality of life, and respiratory rate at 5 weeks. Results: Of 46 patients, 42 were analyzed. Compared to baseline, after 5 weeks, anxiety, dyspnoea, respiratory rate, and most quality of life subscores significantly improved. The Nijmegen score decreased by 11.6 points ( p < 0.001) and this decrease was maintained after 3 months. Conclusions: HVS symptoms have significantly decreased while anxiety, depression, dyspnoea, and quality of life substantially improved after six respiratory physiotherapy sessions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".