Long-term impact of sarcopenia in older patients undergoing gastrectomy for gastric cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Sarcopenia is an inevitable problem in older patients. After gastrectomy, patients often have an inadequate dietary intake and easily fall into sarcopenia. However, the impact of preoperative sarcopenia on long-term outcomes after gastrectomy has not been analyzed. Methods: A systematic review was conducted for all relevant articles identified on PubMed, the Cochrane Library, Web of Science, and ClinicalTrials.gov until April 2023. Adjusted hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using the fixed or random effects model according to the heterogeneity. The Newcastle-Ottawa Scale was used to quantify study quality. Results: Seven studies involving 1,831 patients aged ≥65 years who underwent gastrectomy for gastric cancer were analyzed. Four hundred twelve patients (22.5%) were diagnosed with sarcopenia. The analysis showed that preoperative sarcopenia was significantly associated with poor overall survival (OS) (HR =1.93; 95% CI:1.60-2.34; P<0.001). Two of the included studies also showed that preoperative sarcopenia was significantly correlated with disease-related survival: one with disease-specific survival (DSS) (HR =4.00; 95% CI: 1.20-13.3, P=0.024) and the other with non-cancer specific survival (HR =3.27; 95% CI: 1.61-6.67; P=0.001). Furthermore, sarcopenic patients experienced more severe complications than non-sarcopenic patients (OR =1.80; 95% CI: 1.10-2.95; P=0.019). Conclusions: This meta-analysis suggested that preoperative sarcopenia is useful as a prognostic factor of impaired OS in older patients after gastrectomy. Preoperative evaluation and intervention for skeletal muscle loss should be considered. Further studies of sarcopenic impact on disease-related survival are required.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.002 | 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".