Host phenotype is associated with reduced survival independent of tumour biology in patients with colorectal liver metastases
Bibliographic record
Abstract
BACKGROUND: Most prognostic scoring systems for colorectal liver metastases (CRLMs) account for factors related to tumour biology. Little is known about the effects of the host phenotype to the tumour. Our objective was to delineate the relationship of systemic inflammation and body composition features [i.e. low skeletal muscle mass (sarcopenia) and low visceral adipose tissue (VAT)], two well-described host phenotypes in cancer. METHODS: Clinical data and pre-operative blood samples were collected from 99 patients who underwent resection of CRLM. Pre-operative computed tomography scans were available for 97 patients; body composition was analysed at the L3 level, stratified for sex and age. Clinicopathological variables, serum C-reactive protein (CRP), and various body composition variables were evaluated. Overall survival was evaluated as a function of these same variables in multivariate Cox regression analysis. RESULTS: Skeletal muscle was significantly correlated with VAT (r = 0.46, P < 0.001). Of patients with sarcopenia, 35 (65%) also had low VAT. C-reactive protein was elevated (≥5 mg/mL) in 42 patients (43.3%). Elevated CRP was more common in patients with sarcopenia (73.8% vs. 51.1%, P = 0.029). The most significant prognostic factors were the coincidence of elevated CRP and adverse body composition features (sarcopenia and/or low VAT; hazard ratio 4.3, 95% confidence interval 1.5-13.0, P = 0.008), as well as Fong clinical prognostic score (hazard ratio 2.9, 95% confidence interval 1.5-5.5, P = 0.002). CONCLUSIONS: Body composition in patients with CRLM is not directly linked to the presence of systemic inflammation. However, when systemic inflammation coincides with sarcopenia and/or low VAT, prognosis is adversely affected, independent of the Fong clinical prognostic score.
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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) | 0.002 | 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".