Radiologic Features of T10 Paravertebral Muscle Sarcopenia: Prognostic Factors in COVID-19
Bibliographic record
Abstract
Background: Sarcopenia, defined as a small cross-sectional area (CSA) in computed tomography (CT) measurements of skeletal muscles, serves as a disease severity marker in various clinical scenarios, including pulmonary conditions and critical illness. Another parameter of sarcopenia, the level of myosteatosis, reflected by the tissue's radiodensity, in the thoracic skeletal muscles group, has been linked to disease progression in coronavirus disease 2019 (COVID-19) patients. We hypothesize that CT-derived measurements of the skeletal muscle density (SMD) and the CSA of thoracic skeletal muscles can predict outcomes in COVID-19 pneumonia. Methods: We retrospectively reviewed the CT scans of 84 patients with COVID-19 pneumonia admitted to two of Greece's largest academic teaching hospitals between April 2020 and February 2021. CSA and SMD at the level of the T10 vertebra were measured using computational imaging methods. The patient population was stratified according to survival status and CT severity score (CT-SS). Correlations were drawn between the radiologic features of sarcopenia, CT severity subgroups, serum inflammatory markers, and adverse events, e.g., death and intubation. Results: Thoracic muscles' CSA measurements correlate with CT-SS and prominent inflammatory markers, such as white blood cell (WBC), C-reactive protein (CRP), fibrinogen, and D-dimers. Moreover, according to linear regression analysis, CSA seems to predict CT-SS variation significantly (β = -0.266, P = 0.018). CSA proved to differ significantly across survivors (P = 0.027) but not between CT severity categories and intubation subgroups. The AUC (area under the curve) of the receiver operating characteristic (ROC) curve for the predictive value of thoracic muscles' CSA in mortality is 0.774 (95% confidence interval (CI): 0.66 - 0.83, P < 0.000). The optimal cut-off value (Youden index = 0.57) for mortality prognosis, with a sensitivity of 66.7% and a specificity of 88.9%, is 15.55. Thoracic muscles' SMD analyses did not reveal any significant correlations. Conclusions: Easy to obtain and accurately calculated, radiologic features can provide a reliable alternative to laboratory methods for predicting survival in COVID-19. Thoracic muscles' CSA measurement in the level of the T10 vertebra, an acclaimed prognostic imaging assessment that relates directly to CT-SS and inflammatory markers in COVID-19 pneumonia, is a fairly specific tool for survival prognosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".