Screening for low muscularity in colorectal cancer patients: a valid, clinic‐friendly approach that predicts mortality
Bibliographic record
Abstract
BACKGROUND: Low skeletal muscle quantified using computed tomography (CT) scans is associated with morbidity and mortality among cancer patients. However, existing methods to assess skeletal muscle from CT are time-consuming, expensive, and require training. Clinic-friendly tools to screen for low skeletal muscle in cancer patients are urgently needed. METHODS: ). A subset of CT scans was assessed twice by two analysts to compute intra-rater and inter-rater reliability. We derived cut-points for 'low' linear area using optimal stratification and then calculated the sensitivity and specificity of these cut-points relative to standard methods (total L3 cross-sectional area assessed with Slice-O-Matic research software). We further evaluated the association of low linear area with death from any cause after colorectal cancer diagnosis in Cox proportional hazards models adjusting for demographics, smoking, body mass index category, and tumour characteristics. RESULTS: The linear area was highly correlated with total cross-sectional area assessed using standard methods [r = 0.92; 95% confidence interval (CI): 0.91, 0.93] overall and within subgroups defined by age, sex, and body mass index group. Intra-rater and inter-rater reliability were equally high (both intra-class correlations = 0.98). Cut-points for low linear area were sensitive (0.75; 95% CI: 0.70, 0.80) and specific (0.77; 95% CI: 0.73, 0.80) for identifying low skeletal muscle relative to the standard of total L3 cross-sectional area. The hazard ratio and 95% CI for death associated with a low linear area were hazard ratio = 1.66; 95% CI: 1.22, 2.25. CONCLUSIONS: Clinic-friendly methods that assess linear area from CT scans are an accurate screening tool to identify low skeletal muscle among non-metastatic colorectal cancer patients. These linear measures are associated with mortality after colorectal cancer, suggesting they could be clinically useful both to improve prognostication and to provide a practical screening tool to identify cancer patients who require nutrition or exercise intervention.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".