CORRECTION OF SARCOPENIA IN ELDERLY PATIENTS WITH POST-COVID-19 SYNDROME USING PHYSICAL THERAPY
Bibliographic record
Abstract
Purpose – to determine the effectiveness of the developed physical therapy program for the elderly with post-COVID syndrome according to the dynamics of sarcopenia parameters. Material. 108 elderly people were examined. The control group consisted of 33 people who did not suffer from the coronavirus disease. The main group consisted of 75 people who fell ill with coronavirus pneumonia and diagnosed with post-COVID syndrome. The main group 1 consisted of 34 people who underwent rehabilitation in polyclinic conditions according to the general principles of the Protocol for providing rehabilitation care to patients with COVID-19. The main group 2 consisted of 41 patients who underwent recovery according to the developed rehabilitation program, which included therapeutic exercises of various orientations, functional training, independent implementation of the Otago exercise program complex, a course of general massage, ergotherapeutic methods, nutritional recommendations, patient education taking into account the peculiarities of pathogenesis and clinical course of geriatric syndromes. The effectiveness of the program was evaluated according to the dynamics of indicators of the whale dynamometry, Short Physical Performance Battery, Edmonton Frail Scale. The results. In elderly patients with post-COVID syndrome, signs of muscle weakness (according to hand dynamometry), impaired balance (Short Physical Performance Battery), deterioration of activities of daily living (according to the Edmonton Frail Scale) were found. The developed rehabilitation program revealed a statistically significantly better effect on indicators of strength, balance, and the state of physical weakness in comparison with the initial indicators in all studied parameters (р<0.05). The creation of a rehabilitation program taking into account the peculiarities of the course of geriatric syndrome increases its effectiveness, which is demonstrated by a statistically significantly better result of leveling the signs of sarcopenia in patients with post-COVID syndrome compared to the group that practiced according to the general recommendations of the clinical protocol. Conclusions. Elderly patients with post-COVID syndrome and sarcopenia need the development of physical therapy programs taking into account and correcting the specifics of each condition, the presence of physical status disorders and impaired performance of activities of daily living.
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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 | 0.000 |
| 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) | 0.001 | 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".