Pulmonary rehabilitation in a postcoronavirus disease 2019 world: feasibility, challenges, and solutions
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pulmonary rehabilitation improves clinical outcomes in patients with chronic obstructive pulmonary disease (COPD). Traditional centre-based (in-person) pulmonary rehabilitation was largely shut down in response to the COVID-19 pandemic, forcing many centres to rapidly shift to remote home-based programs in the form of telerehabilitation (tele-pulmonary rehabilitation). This review summarizes the recent evidence for the feasibility and effectiveness of remote pulmonary rehabilitation programs, and their implications for the delivery of pulmonary rehabilitation in a postpandemic world. RECENT FINDINGS: A number of innovative adaptations to pulmonary rehabilitation in response to COVID-19 have been reported, and the evidence supports tele-pulmonary rehabilitation as a viable alternative to traditional centre-based pulmonary rehabilitation. However, these studies also highlight the challenges that must be surmounted in order to see its widespread adoption. SUMMARY: There are outstanding questions regarding the optimal model for tele-pulmonary rehabilitation. In the post-COVID-19 world, a 'hybrid' model may be more desirable, with some components held in person and others via telehealth technology. This would be determined by the infrastructure and expertise of individual centres, and the needs of their patients. In order to achieve a truly patient-centred pulmonary rehabilitation program, high-quality studies addressing these outstanding questions, as well as multidisciplinary collaboration, are required.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".