Sarcopenia Determined by Skeletal Muscle Index Predicts Overall Survival, Disease-free Survival, and Postoperative Complications in Resectable Esophageal Cancer
Bibliographic record
Abstract
BACKGROUND: Sarcopenia has been identified as a prognostic factor among certain types of cancer. In esophageal cancer, patients are at increased risk of malnutrition and sarcopenia, ultimately contributing to poor outcomes. A systematic review was conducted to determine whether sarcopenia, defined by the skeletal muscle index, is predictive of overall survival, disease-free survival, and postoperative complications in resectable esophageal cancer. MATERIALS AND METHODS: A systematic search of MEDLINE, EMBASE, Scopus, Web of Science, and Cochrane Library was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines up until January 2021. The primary outcome was overall survival; secondary outcomes included disease-free survival, pulmonary complications, and anastomotic leak. RESULTS: Twenty-one studies (4 prospective; 17 retrospective; 3966 patients) were included. Sarcopenia was present in 1940 (48.1%) patients and was associated with lower overall survival [hazard ratio (HR): 1.56; 95% confidence interval (CI): 1.25-1.95; P <0.00001; I2 =71%] and disease-free survival (HR: 1.73; 95% CI: 1.04-2.87; P =0.03; I2 =51%). A decrease in skeletal muscle index, independent of sarcopenia status, was associated with lower overall survival (HR: 1.81; 95% CI: 1.20-2.73; P =0.005; I2 =92%). Sarcopenia was associated with increased odds of pulmonary complications (odds ratio: 1.86; 95% CI: 1.29-2.66; P =0.0008; I2 =41%) and increased odds of anastomotic leak (odds ratio: 1.46; 95% CI: 1.11-1.93; P =0.008; I2 =0%). CONCLUSIONS: Sarcopenia is a predictor of overall survival, disease-free survival, and postoperative complications in patients with resectable esophageal cancer. Studies on the modifiability of sarcopenia in the preoperative period will help determine the utility of nutritional interventions.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".