The prognostic value of sarcopenia in oesophageal cancer: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract The loss of skeletal muscle mass and function is defined as sarcopenia, which might develop in elderly patients with cancers. It has been indicated as a potential negative factor in the survival of patients with malignant tumours. The aim of this systematic review and meta‐analysis was to evaluate the associations between sarcopenia and survival outcomes or postoperative complications in patients with oesophageal cancer (EC). Web of Science, Embase, Medline, and Cochrane Library databases were searched until 10 May 2022, using keywords: sarcopenia, oesophageal cancer, and prognosis. Studies investigating the prognostic value of sarcopenia on EC survival were included. Forest plots and summary effect models were used to show the result of this meta‐analysis. The quality of included studies was evaluated with the Newcastle‐Ottawa Scale (NOS). A total of 1436 studies were identified from the initial search of four databases, and 41 studies were included for the final quantitative analysis. This meta‐analysis revealed a significant association between sarcopenia and overall survival (OS) [hazard ratios (HR):1.68, 95% confidence interval (CI):1.54–1.83, P = 0.004, I 2 = 41.7%] or disease‐free survival (DFS) 1.97 (HR: 1.97, 95% CI: 1.44–2.69, P = 0.007, I 2 = 61.9%) of EC patients. Subgroup analysis showed that sarcopenia remained a consistent negative predictor of survival when stratified by different treatment methods, populations, or sarcopenia measurements. Sarcopenia was also a risk factor for postoperative complications with a pooled odds ratio of 1.47 (95% CI: 1.21–1.77, P = 0.094, I 2 = 32.7%). The NOS scores of all included studies were ≥6, and the quality of the evidence was relatively high. The results from the study suggested that sarcopenia was significantly associated with both survival outcomes and postoperative complications in EC patients. Sarcopenia should be appropriately diagnosed and treated for improving short‐term and long‐term outcomes of patients with EC.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".