Anterior Chest Physiotherapy and Breathing Exercises for Cardiac Surgery Patients; A Cross Sectional Survey
Bibliographic record
Abstract
Background: Coronary artery disease introduced as the most prevalent cause of mortality all over the world. There is limited published data on which type of chest physiotherapy and breathing exercises are recommended to cardiac patients after surgery. Objectives: To determine the prevalence of chest physiotherapy and breathing exercise for the post cardiac patients. To determine the type of chest physiotherapy and breathing exercise for the post cardiac patients. Methodology: A Cross-sectional survey was conducted on 150 physiotherapists from March to August 2018 selected via convenience sampling technique. Data was collected from Tabba cardiac Hospital and NICVD Karachi, using self-structured questionnaire for prevalence and type of exercise. Data was analyzed using SPSS v.21. Results: According to the results, the prevalence of chest physiotherapy and breathing exercise for the cardiac patients was 80%. The most frequent type of exercise was deep breathing 92%, diaphragmatic breathing 90%, coughing80%, relaxation techniques 72% and chest wall Vibration 70%. The less used treatments were positive expiratory pressure (PEP) device breathing (21.3%) and inspiratory resistance positive expiratory pressure (IR-PEP) (12.5%). Recommendations to continue breathing exercises after discharge varied from not at all up to 3 months after surgery. Conclusion: Physiotherapy and Breathing exercise are mostly used in post-operative cardiac procedures. Every Hour breathing activities, diaphragmatic breathing and pressed together lip breathing exercise the highly recommended regime practiced. Guideline for the duration after discharge varies. Keywords: Chest Physical Therapy (CPT), breathing exercise, cardiac patient, ACBTs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".