The Impact of Sarcopenia on Postoperative Outcomes in Colorectal Cancer Surgery: An Updated Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Sarcopenia is thought to be a marker for underlying frailty and malnutrition, contributing to poor functional status and suboptimal healing postoperatively. We aimed to complete an updated systematic review and meta-analysis evaluating the impact of sarcopenia on short- and long-term outcomes following colorectal cancer surgery. We searched MEDLINE, Embase, and CENTRAL up to September 2023. Studies that compared sarcopenic and non-sarcopenic patients’ short- and long-term outcomes following curative intent elective surgery for colorectal cancer were included. The main outcomes included postoperative morbidity, postoperative mortality, and length of stay (LOS), among others. Inverse variance random effects meta-analyses was performed. Risk of bias was assessed with Cochrane tools. Certainty of evidence was assessed with GRADE. After screening 215 studies, we included 40 non-randomized studies, totalling 13,422 patients, of which 5,432 (40.4%) were classified as sarcopenic. Across 27 studies, patients with sarcopenia were more likely to experience 30-day postoperative morbidity (40% vs 33%, RR 1.30, 95% CI 1.12-1.50, P < 0.01, I 2 79%). The mean LOS was 1.46 days longer for sarcopenic patients (26 studies, 95% CI 0.85-2.07, P < 0.01, I 2 82%). Upon pooling data from 13 studies, sarcopenic patients had increased risk of 30-day postoperative mortality (2.8% vs 1.0%, RR 2.74, 95% CI 1.63-4.62, P < 0.01, I 2 0%). The findings from this systematic review suggest with low to very-low certainty evidence that in patients who are undergoing curative intent surgery for colorectal cancer, preoperative sarcopenia is associated with poor postoperative outcomes.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".