Thoracic muscle mass predicts survival among patients with locally advanced esophageal cancer
Bibliographic record
Abstract
Background & aims There is limited literature evaluating muscle mass at the fourth thoracic (T4) vertebrae using computed tomography (CT) images, with no studies evaluating T4 muscle mass in esophageal cancer. Methods In this retrospective cohort study, body composition analysis using skeletal muscle index (SMI) was conducted at T4 and L3. Overall survival (OS) and disease-free survival (DFS) were evaluated using Kaplan–Meier curves and log-rank tests, as well as multivariable cox proportional hazards models. Correlation analysis and evaluation of fixed and proportional bias was conducted. Low muscle mass was defined by the lowest quartile of the SMI distribution from the post-neoadjuvant CT: <30.4 cm 2 /m 2 (females) and <42.2 cm 2 /m 2 (males) for L3, and <35.4 cm 2 /m 2 (females) and <52.6 cm 2 /m 2 (males) for T4. Results Of the 120 patients included, eight (8.2 %) patients had T4-low muscle mass at the staging CT which increased to 25 (25.8 %) at the post-neoadjuvant CT. On multivariable analysis, T4-low muscle mass was associated with worse overall survival (OS) (HR 2.51, 95 % CI 1.47–4.29, p=0.001) and disease-free survival (DFS) (HR 1.88, 95 % CI 1.09–3.24, p=0.022). T4-SMI was higher than L3-SMI at both the staging (65.4 ± 13.6 cm 2 /m 2 versus 51.1 ± 10.0 cm 2 /m 2 , p < 0.001) and post-neoadjuvant (57.8 ± 12.7 cm 2 /m 2 versus 45.8 ± 9.3 cm 2 /m 2 , p < 0.001) CT scans. The correlation (R-value) between T4 and L3 SMI was greater than 0.6 (0.62–0.81) for all staging intervals. Conclusion Our findings support using low muscle mass at T4 as a prognostic indicator for OS and DFS. These findings can be extrapolated to tumor groups, such as lung cancer, where L3-low muscle mass status is not routinely available.
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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 | 0.001 |
| 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 | 0.001 |
| 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".