Pulmonary telerehabilitation for respiratory sequelae post-COVID: RCT preliminary analysis
Bibliographic record
Abstract
Introduction: Post-COVID respiratory sequelae are common. Aim: To evaluate pulmonary telerehabilitation (PTR) for people with persistent respiratory symptoms post-COVID. Methods: Participants were recruited from a Post-COVID Respiratory Clinic and randomised to an intervention group (IG) (4-week, twice-weekly supervised PTR, or a usual care control group (CG) who could crossover to the IG post control period. Exercise intensity in the IG was titrated to ensure post-exercise fatigue <3 out of 10. Remote assessments pre/post the intervention and control periods included: 1-minute sit-to-stand test (1minSTST); 5 repetition sit-to-stand test (5STST); Montreal Cognitive Assessment (MoCA-BLIND); COPD Assessment Test (CAT); Hospital Anxiety and Depression Scale (HADS); Fatigue Severity Scale (FSS). Analysis used repeated measures ANOVA. Results: Participants completed the IG (n=27) and CG (n=25). Mean(SD) age was 55(16) years, body mass index 31(8)kg/m2, 60% female, and 75% were not hospitalised for their COVID infection. Baseline values for the IG included mean(SD) 1minSTST 20(7)reps, 5STST 14(8)sec, MoCA-Blind 19(3), all within normal range. There were no significant between-group differences for any outcomes, mean difference(95%CI) 1minSTST 1.1(−1.7 to 3.9); 5STST 0.6(−1.9 to 3.1); MoCA-BLIND -0.2(−1.6 to 1.1); CAT-2.2(−5.9 to 1.4); Anxiety 0.6(−0.9 to 2.1); Depression 0.7(−0.7 to 2.2); FSS -0.9(−6.6 to 4.7). Conclusion: At study completion, there was no significant difference between short-term PTR and usual care. The lack of difference may have been due to normal baseline physical and cognitive function in the IG group, participant fatigue guiding exercise intensity, and a short intervention.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".