Recommendations and Effects of Rehabilitation Programs in Older Adults After Hospitalization for COVID-19
Bibliographic record
Abstract
ABSTRACT: The aims of this review were to identify studies on physical rehabilitation programs and describe the potential effects on functional outcomes in patients older than 60 yrs at discharge from acute care post-COVID-19. The literature search was conducted in the MEDLINE, Cochrane CENTRAL, EMBASE, PEDro, LILACS, CINAHL, SPORTDiscus, Web of Science, and The Living OVerview of Evidence (L-OVE) COVID-19 databases. Studies with patients older than 60 yrs, hospitalized with COVID-19, and admitted to a rehabilitation program after discharge from acute care were included. Ten studies were included with a total of 572 patients. The prevalence of patients who received post-intensive care rehabilitation was 53% (95% confidence interval, 0.27-0.79; P = 0.001). The rehabilitation program included physiotherapy in nine studies, occupational therapy in three studies, and psychotherapy in two studies. The rehabilitation programs increased aerobic capacity, functional independence in basic activities of daily living, muscle strength, muscle mass, dynamic balance, physical performance, pulmonary function, quality of life, cognitive capacity and mental health. Multidisciplinary rehabilitation programs are necessary for older adults after hospitalization for COVID-19, especially those coming from intensive care units, as rehabilitation has a positive effect on important clinical outcomes.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".