Sarcopenia, sarcopenic obesity, myosteatosis as factors of poor prognosis in gastrointestinal tract tumors: sistematic review
Bibliographic record
Abstract
Background. Gastrointestinal (GI) tract cancer includes a broad spectrum of tumors with generally high prevalence and poor prognosis. Over the past decade sarcopenia (skeletal muscle depletion), myosteatosis, sarcopenic obesity were all shown to have a negative prognostic impact in patients with various malignancies. However, the role of myosteatosis in patients with GI tumors remains controversial. Aim. To summarize recent literature regarding the impact of myosteatosis on the surgical treatment of patients with GI malignancies. Materials and methods. PubMed, Cochrane Library and ClinicalTrials.gov databases were searched for relevant original studies published between Jan. 2011 and Dec. 2021. The risk of bias of the included studies was assessed using Newcastle-Ottawa Scale (NOS). Results. 34 studies comprising 15 295 patients were included. Patients with myosteatosis had significantly poorer overall survival (hazard ratio 0,506, 95% confidence interval 0,4310,595; p0,05). There was no significant influence of myosteatosis on recurrence-free survival (hazard ratio 0,658, 95% confidence interval 0,3891,112; p0,05). Myosteatosis was significantly associated with the occurrence of major postoperative complications in 6 studies. However, only 3 studies supported the impact of myosteatosis on mortality. Conclusion. This meta-analysis demonstrates that patients with preoperative myosteatosis have poor long-term survival following treatment for GI malignancy. Therefore, myosteatosis might be used as a prognostic tool. However, more studies with standardized definitions and cut-offs 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".