Thoracic muscle cross-sectional area is associated with hospital length of stay post lung transplantation: a retrospective cohort study
Bibliographic record
Abstract
Low muscle mass is common in lung transplant (LTx) candidates; however, the clinical implications have not been well described.The study aims were to compare skeletal muscle mass in LTx candidates with controls using thoracic muscle cross-sectional area (CSA) from computed tomography and assess the association with pre-and post-transplant clinical outcomes.This was a retrospective, single-center cohort study of 527 LTx candidates [median age: 55 IQR (42-62) years; 54% male].Thoracic muscle CSA was compared to an age-and sex-matched control group.Associations between muscle CSA and pre-transplant six-minute walk distance (6MWD), health-related quality of life (HRQL), delisting/mortality, and post-transplant hospital outcomes and one-year mortality were evaluated using multivariable regression analysis.Muscle CSA for LTx candidates was about 10% lower than controls (n = 38).Muscle CSA was associated with pre-transplant 6MWD, but not HRQL, delisting or pre-or post-transplant mortality.Muscle CSA (per 10 cm 2 difference) was associated with shorter hospital stay [0.7 median days 95% CI (0.2-1.3)], independent of 6MWD.In conclusion, thoracic muscle CSA is a simple, readily available estimate of skeletal muscle mass predictive of hospital length of stay, but further study is needed to evaluate the relative contribution of muscle mass versus functional deficits in LTx candidates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".