Prognostic significance of osteosarcopenia and its effects on immune response in patients with stage II/III gastric cancer
Bibliographic record
Abstract
BACKGROUND: Osteosarcopenia, characterized by muscle loss and osteoporosis, has emerged as a prognostic marker for various malignancies. However, its impact on the immune response in gastric cancer remains unclear. This study aimed to assess the clinical significance of osteosarcopenia and its relationship with the immune microenvironment in patients with advanced gastric cancer. METHODS: This study included 105 patients with pathological stage II/III gastric cancer who underwent gastrectomy between 2018 and 2022. Preoperative computed tomography was used to measure muscle mass and bone density, with sarcopenia and osteoporosis defined as values below the respective standard thresholds. Sarcopenia and osteoporosis were identified when both conditions were present. We explored the relationships between osteosarcopenia, clinicopathological factors, and prognoses. Additionally, immune marker expression was evaluated via immunohistochemistry. RESULTS: Among the 105 patients, 37 (35%) were diagnosed with osteosarcopenia. This condition significantly correlated with performance status, body mass index, and disease recurrence (all p < 0.05). Overall survival and relapse-free survival were significantly lower in the osteosarcopenia group than those in the non-osteosarcopenia group (all p < 0.05). Moreover, the osteosarcopenia group had significantly fewer CD8-positive, programmed cell death protein 1-positive, and programmed death-ligand 1-positive cells than that of the control group (all p < 0.05). CONCLUSIONS: Our findings suggest that osteosarcopenia is associated with the tumor microenvironment and might serve as a prognostic indicator in patients with advanced gastric cancer.
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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".