Virtual Physical Prehabilitation in Lung Transplant Candidates: A Proof-of-Concept Study
Bibliographic record
Abstract
This study aimed to preliminary test the effectiveness of 12-week virtual physical prehabilitation program followed by a maintenance phase. The main objective was to estimate the extent to which it affects exercise capacity, frailty, lower limb strength and health-related quality of life (HRQOL) in lung transplant candidates. The program offered supervised strengthening exercises, independent aerobic exercises and weekly phone calls (maintenance phase). Primary outcome was the six-minute walk distance (6MWD). Secondary outcomes: the Short Physical Performance Battery (SPPB), five-times sit-to-stand test (5STS), the St George's Respiratory Questionnaire (SGRQ) for HRQOL. Twenty patients were included (mean age 57.9; 6 women/14 men); fourteen completed the prehabilitation program and 5 completed the maintenance phase. There was no statistically significant improvement in 6MWD, SPPB or SGRQ after the 12-week program. Most patients either maintained or improved the 6MWT and SPPB scores. There was a significant improvement in the 5STS. After the maintenance phase, most patients either improved or maintained their scores in all outcomes except for the sub-score of symptoms in the SGRQ. A 12-week virtual physical prehabilitation program with a 12-week maintenance phase can help lung transplant candidates improve or maintain their physical function while waiting for transplantation.
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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 | 0.000 |
| 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".