Muscle wasting, visceral and subcutaneous adiposity, inflammation, nutritional deficiencies, and metastatic esophageal cancer (MEC) prognosis.
Bibliographic record
Abstract
e14595 Background: MEC, associated with fatigue and dysphagia, leads to loss of skeletal muscle mass, malnutrition, subcutaneous and visceral adiposity. Cancer inflammation mobilizes muscle and adipose tissue, potentially leading to cachexia and sarcopenia. Supportive management depends on understanding the cancer frailty determinants that lead to poor outcomes. Methods: We retrospectively identified de novo MEC patients pts treated in Toronto, Canada (2007-2014). Body composition including visceral (VA) and subcutaneous adiposity (SA) at L3 level were assessed with baseline CT scans using SliceOMatic software by two outcome-blinded radiologists (Intraclass correlation, 0.92-1.00). Sarcopenia was assessed using Skeletal Muscle Index (SMI) with cut-offs defined either by optimized-stratification (OpS) or gender-dependent consensus cutoffs (GdC). Cox proportional hazard models generated adjusted hazard ratios (aHR). Results: Of 101 patients, 82% were male; 96% Caucasian; median age at diagnosis 61y (29-88); mean body mass index (BMI) 25.4; 69%/31% adeno/squamous cell carcinoma; median overall and progression free survival were: 6.4 (OS) and 3.9 mos (PFS). Median follow-up time was 5.6 mos. SMI-OpS and SMI-GdC were correlated (Rho = 0.67). Nutritional risk index, BMI, neutrophil-to-lymphocyte and neutrophil-to-platelet ratios were not associated with outcome (p > 0.20, each comparison). However, univariable analyses identified serum albumin, LDH, and either SMI-OpS or SMI-GdC as being associated with OS. In multivariable models, sarcopenia was associated with worse OS (SMI-OpS aHR = 1.93 (1.0-3.7) p = 0.046; SMI-GdC aHR = 2.30 (1.3-4.1) p = 0.004), and worse PFS (SMI-OpS aHR = 2.16 (1.2-4.0) p = 0.01; SMI-GdC aHR = 1.66 (1.0-2.9) p = 0.07)). In 55 pts receiving chemotherapy at diagnosis, less VA (p = 0.01) and SA (p = 0.02), as continuous variables, were associated with worse OS. Conclusions: Though no associations were found between nutritional deficiencies or inflammatory markers and prognosis, there was approximately a two-fold worse prognosis in the presence of sarcopenia, and associations with loss of adiposity. (JB/AFF/DR/MM contributed equally).
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".