Computed Tomography–Derived Thoracic Muscle Size as an Indicator of Sarcopenia in People With Advanced Lung Disease
Bibliographic record
Abstract
Purpose: Computed tomography (CT) of the chest is routinely performed in people with lung disease; however, the utility of measuring thoracic muscle size to assess the presence of sarcopenia (low muscle mass and function) has not been studied. The purpose of this study was to examine the reliability and validity of thoracic muscle size obtained from chest CT as a surrogate of sarcopenia. Methods: In this observational study, chest CT was obtained from routine clinical evaluation in 32 individuals with advanced lung disease awaiting lung transplantation. Thoracic muscle area from vertebral levels T4–T6 was manually segmented using Slice-O-Matic software, and average muscle cross-sectional area (CSA) and muscle volume were calculated. Measures of sarcopenia included quadriceps CSA and thickness from ultrasound, quadriceps, and biceps torque and short physical performance battery (SPPB). Results: Intrareliability and interrater reliability for muscle CSA were high (intraclass correlation coefficient = 0.96, 0.99; absolute difference = 0.61, 1.7 cm2, respectively). Thoracic muscle CSAs and volume correlated with quadriceps size and limb muscle strength (r = 0.56–0.71, P < .001) but not SPPB. Cross-sectional areas from single slices at T4–T6 were highly correlated with muscle volume (r = 0.89–0.91, P < .001). Conclusions: Thoracic muscle size seems to be a reliable and valid technique that can be applied in large studies evaluating the presence of sarcopenia in patients with advanced lung disease.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".