Effectiveness of Physiotherapy Treatment on Clinical Features and Quality of Life of COVID-19 Positive Patients
Bibliographic record
Abstract
Background: World Health Organization has declared corona virus disease (COVID 19) a global pandemic. Fever, dry cough and breathlessness are the main manifestations. In addition, myalgia, muscle weakness, fatigue may render the patients unable to carry out activities of daily living (ADL) which affect patient’s QOL. Amongst health workers, physiotherapists are playing an important role in managing and caring novel COVID-19 patients in ICUs and also out there. Clinical features-Pain, Muscle weakness, Fatigue was assessed by following Numeric pain rating scale, Manual muscle testing, Fatigue assessment scale. Functional status and Quality of life of patients, measured by functional independent measurement scale, McGill Quality of life Questionnaire. So, aim is to provide evidence of physiotherapist involvement in managing COVID-19 infected patients. Materials and Methods: Interventional study, simple random sampling with sample size of sixteen Covid 19 positive patients was done. Both male and female included. Patients who were mentally ill excluded. Oral consent was taken. Subjects were given physiotherapy treatment for 1 week. (Seven days) which include chest physiotherapy, Breathing exercises, early mobilization, Limb physiotherapy, Positioning etc. Clinical features of Covid 19-Pain, Muscle strength, Fatigue level, Functional status and Quality of life was measured before and after intervention. Wilcoxon test used to analyze data. Results: Data of 16 subjects was analyzed. Result showed that significant decrease in pain, fatigue level and increase in muscle strength, functional status and QOL. Conclusion: Physiotherapy treatment improves QOL and clinical features of covid 19 patients like pain, muscle strength, fatigue. Key words: Physiotherapy, Pain, Muscle strength, Fatigue, functional level, QOL.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".