Rehabilitation of patients after COVID-19 recovery: An experience at the Physical and Rehabilitation Medicine Institute and Lucy Montoro Rehabilitation Institute
Bibliographic record
Abstract
OBJECTIVES: As patients recovering from the novel coronavirus disease 2019 (COVID-19) present with physical, respiratory, cognitive, nutritional, and swallowing-related impairments and mental health complications, their rehabilitation needs are complex. This study aimed to describe the demographic, clinical, and functional status after the discharge of COVID-19 survivors who underwent intensive multidisciplinary inpatient rehabilitation at the Physical and Rehabilitation Medicine Institute of the University of Sao Paulo Medical School General Hospital and Lucy Montoro Rehabilitation Institute. We determined the most important factors related to the length of inpatient rehabilitation treatment and present the functional outcomes. METHODS: This was a retrospective study based on electronic medical records. In addition to the severity of COVID-19 and length of hospital stay for the management of COVID-19 and comorbidities, we collected sociodemographic data including age, sex, height, and weight. Functional assessments were performed using the Functional Independence Measure (FIM); Short Physical Performance Battery; Montreal Cognitive Assessment; Depression, Anxiety and Stress Scale; Revised Impact of Events Scale; bioelectrical impedance; Functional Oral Intake Scale; oropharyngeal dysphagia classification; and nutritional assessment. RESULTS: There was a significant improvement in FIM before and after inpatient rehabilitation treatment (p<0.0001). Muscle strength and walking capacity were significantly improved (p<0.01). The most important factors related to the length of inpatient rehabilitation treatment were improvement in FIM scores (Spearman's r=0.71) and gain in lean mass (Spearman's r=0.79). CONCLUSIONS: Rehabilitation of patients after COVID-19 recovery improves their functional status and should be considered in the post-acute phase for selected patients with COVID-19.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".