Understanding outcomes of virtual rehabilitation in long COVID: post-hoc analysis of a randomized controlled trial (RCT)
Bibliographic record
Abstract
Background Our RCT on virtual rehabilitation in individuals with long COVID revealed that 39% of the intervention group participants could not progress through the exercise training due to symptoms. We aimed to assess how those who progressed through the exercise training differed from those who did not, based on baseline characteristics and clinical outcomes. Methods The RCT randomized 132 individuals with long COVID to an 8-week virtual exercise program or usual care. Outcomes included mobility, symptoms, post-exertional malaise (PEM), physical function, quality of life, mental health, healthcare use and adverse events (AEs). A post-hoc analysis compared individuals who progressed through the exercise training (progress group, n=36) with those who did not (non-progress group, n=25). Results There were no statistically significant differences in baseline characteristics between groups. The non-progress group showed smaller improvements in 1-minute sit-to-stand test (difference in change: -3.5; 95% CI -6.1,-0.9), timed up and go test (difference in change: 1.47; 95% CI 0.01,2.93), EQ-5D-5L VAS 0-100 (difference in change: -9.73; 95% CI -18.75,-0.71), and in PEM severity (difference in change: 1.72; 95% CI 0.57,2.88). The non-progress group had smaller improvement in post-intervention dyspnea (p=0.04) and higher healthcare use (p=0.03). Higher proportion of participants in the non-progress group (64%) reported PEM as AEs versus 22% in the progress group. Conclusion Individuals who did not progress through the exercise training were more likely to have PEM as an AE and did not benefit from the intervention to the same extent as those who progressed through the training.
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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 | 0.032 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".