Sarcopenia and survival in colorectal cancer without distant metastasis: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIM: Despite prior attempts to evaluate the effects of sarcopenia on survival among patients with colorectal cancer (CRC), the results of these studies have not been consistent. The present study aimed to evaluate the association between sarcopenia and survival among patients having CRC without distant metastasis by aggregating multiple studies. METHODS: We performed a literature search using computerized databases and identified additional studies from among the bibliographies of retrieved articles. The quality of each study was evaluated using the Newcastle-Ottawa Scale, and meta-analyses were performed to evaluate overall survival (OS) and disease-free survival (DFS). RESULTS: Thirteen studies with up to 6600 participants were included in the meta-analyses, with a mean age of 63.6 years (range: 18-93 years). We found that preoperative sarcopenia was associated with worse OS (hazard ratio [HR]: 1.61; 95% confidence interval [CI]: 1.38-1.88) and worse DFS (HR: 1.57; 95% CI: 1.10-2.24). Compared with patients without sarcopenia after tumor resection, those with postoperative sarcopenia had worse OS (HR: 1.76; 95% CI: 1.47-2.10) and DFS (HR: 1.79; 95% CI: 1.46-2.20). CONCLUSION: These meta-analyses suggest that sarcopenia, no matter observed before or after tumor resection, is associated with worse OS and DFS in patients with CRC who have no distant metastasis.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".