Telerehabilitation for Post-Hospitalized COVID-19 Patients: A Proof-of-Concept Study During a Pandemic
Bibliographic record
Abstract
PURPOSE: Telerehabilitation could prevent sequelae from COVID-19. We aimed to assess the feasibility of telerehabilitation; describe pulmonary and functional profiles of COVID-19 patients; and explore the effect of telerehabilitation on improving pulmonary symptoms and quality of life. METHODS: We conducted a pre-experimental, pre-post pilot study. We recruited COVID-19 patients who had returned home following hospitalization. The intervention included eight weeks of supervised physiotherapy sessions. We documented technological issues, success of recruitment strategies, and participants' attendance to supervised sessions. We measured the impact of pulmonary symptoms on quality of life and functional health. RESULTS: We scheduled 64 supervised sessions with seven participants with few technological issues. Initial scores showed that pulmonary symptoms moderately to highly impacted quality of life. At eight weeks, all patients had improved from 10 to 45 points on the EuroQol-Visual Analog Scale (EQ-VAS) instrument, indicating clinical significance. CONCLUSION: We developed and administered a telerehabilitation intervention during a global pandemic that targets key symptoms of the relevant disease.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".