Sarcopenia as a prognostic factor for survival in patients with locally advanced gastroesophageal adenocarcinoma
Bibliographic record
Abstract
BACKGROUND AND AIMS: Patients with gastric cancer often show signs of malnutrition. We sought to evaluate the influence of sarcopenia in patients with locally advanced, not metastasized, gastric or gastro-esophageal junction (GEJ) cancer undergoing curative treatment (perioperative chemotherapy and surgery) on morbidity and mortality in order to identify patients in need for nutritional intervention. PATIENTS AND METHODS: Two-centre study, conducted in the Frankfurt University Clinic and Krankenhaus Nordwest (Frankfurt) as part of the University Cancer Center Frankfurt (UCT). 47/83 patients were treated in the FLOT trial (NCT01216644). Patients´ charts were reviewed for clinical data. Two consecutive CT scans were retrospectively analyzed to determine the degree of sarcopenia. Survival was calculated using the Kaplan-Meier method, multivariate analysis was performed using the Cox regression. RESULTS: 60 patients (72.3%) were male and 23 (27.7%) female. 45 patients (54.2%) had GEJ type 1-3 and 38 (45.8%) gastric tumors, respectively. Sarcopenic patients were significantly older than non-sarcopenic patients (mean age 65.1 years vs. 59.5 years, p = 0.042), terminated the chemotherapy significantly earlier (50% vs. 22.6%, p = 0.037) and showed higher Clavien-Dindo scores, indicating more severe perioperative complications (score ≥3 43.3 vs. 17.0%, p = 0.019). Sarcopenic patients had a significantly shorter survival than non-sarcopenic patients (139.6 ± 19.5 [95% CI, 101.3-177.9] vs. 206.7 ± 13.8 [95% CI, 179.5-233.8] weeks, p = 0.004). Multivariate Cox regression analysis showed that, besides UICC stage, sarcopenia significantly influenced survival. CONCLUSION: Sarcopenia is present in a large proportion of patients with locally advanced gastric or GEJ cancer and significantly influences tolerability of chemotherapy, surgical complications and survival.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".