Pulmonary Rehabilitation in a Post-COVID-19 World: Telerehabilitation as a New Standard in Patients with COPD
Bibliographic record
Abstract
Pulmonary rehabilitation (PR) is effective in reducing symptoms and improving health status, and exercise tolerance of patients with chronic obstructive pulmonary disease (COPD). The coronavirus disease 19 (COVID-19) pandemic has greatly impacted PR programs and their delivery to patients. Owing to fears of viral transmission and resultant outbreaks of COVID-19, institution-based PR programs have been forced to significantly reduce enrolment or in some cases completely shut down during the pandemic. As a majority of COPD patients are elderly and have multiple co-morbidities including cardiovascular disease and diabetes, they are notably susceptible to severe complications of COVID-19. As such, patients have been advised to stay at home and avoid social contact to the maximum extent possible. This has increased patients' vulnerability to physical deconditioning, depression, and social isolation. To address this major gap in care, some traditional hospital or clinic-centered PR programs have converted some or all of their learning contents to home-based telerehabilitation during the pandemic. There are, however, some significant barriers to this approach that have impeded its implementation in the community. These include variable access and use of technology (by patients), a lack of standardization of methods and tools for evaluation of the program, and inadequate training and resources for health professionals in optimally delivering telerehabilitation to patients. There is a pressing need for high-quality studies on these modalities of PR to enable the successful implementation of PR at home and via teleconferencing technologies. Here, we highlight the importance of telerehabilitation of patients with COPD in the post-COVID world and discuss various strategies for clinical implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".