Evaluation of Skeletal Muscle Function in Lung Transplant Candidates
Bibliographic record
Abstract
BACKGROUND: Lung transplantation (LTx) is offered to older and more complex patients who may be at higher risk of skeletal muscle dysfunction, but the clinical implications of this remain uncertain. The study aims were to characterize deficits in skeletal muscle mass, strength and physical performance, and examine the associations of these deficits with clinical outcomes. METHODS: Fifty LTx candidates (58% men; age, 59 ± 9 years) were prospectively evaluated for skeletal muscle deficits: muscle mass using bioelectrical impedance, quadriceps, respiratory muscle and handgrip strength, and physical performance with the Short Physical Performance Battery. Comparisons between number of muscle deficits (low muscle mass, quadriceps strength and physical performance) and 6-minute walk distance (6MWD), London Chest Activity of Daily Living Questionnaire, and quality of life were assessed using one-way analysis of variance. Associations with pretransplant and posttransplant delisting/mortality, hospital duration, and 3-month posttransplant 6MWD were evaluated using Fisher exact test and Spearman correlation. RESULTS: Deficits in quadriceps strength (n = 27) and physical performance (n = 24) were more common than muscle mass (n = 8). LTx candidates with 2 or 3 muscle deficits (42%) compared with those without any deficits (26%) had worse 6MWD = -109 m (95% confidence interval [CI], -175 to -43), London Chest Activity of Daily Living Questionnaire = 18 (95% CI, 7-30), and St. George's Activity Domain = 12 (95% CI, 2-21). Number of muscle deficits was associated with posttransplant hospital stay (r = 0.34, P = 0.04), but not with delisting/mortality or posttransplant 6MWD. CONCLUSIONS: Deficits in quadriceps muscle strength and physical performance are common in LTx candidates and further research is needed to assess whether modifying muscle function pretransplant can lead to improved clinical outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".