[ Mucinous Versus Non-Mucinous Gastric Adenocarcinoma: A Systematic Review and Meta-Analysis of Prognostic and Clinicopathological Differences
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Mucinous gastric adenocarcinoma (MGC) is historically linked to poor prognosis, yet literature inconsistencies necessitate systematic evaluation. This meta-analysis aims to compare clinicopathological features and survival outcomes between MGC and non-mucinous gastric adenocarcinoma (NMGC). METHODS: A systematic search (PubMed/Embase/Web of Science, up to April 2024) identified cohort/case-control studies. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated via random-effects models. Subgroup analyses stratified by TNM stage, sample size, geographic region, and T stage were conducted. Heterogeneity, publication bias (Begg’s/Egger’s tests), and sensitivity analyses were assessed. Study quality was appraised using the Newcastle-Ottawa Scale. RESULTS: Twenty-nine studies (163,116 patients; 4,900 MGC) revealed worse univariate overall survival for MGC (HR=1.73, 95%CI=1.48, 2.01), but significance vanished after multivariable adjustment (HR=1.09, 95%CI=0.95, 1.25). Subgroup analyses demonstrated heterogeneity: early-stage MGC (TNM I) had poorer prognosis (HR=1.53, 95%CI=1.07, 2.18), while larger cohorts (>2,000 patients) showed attenuated risk (HR=1.33 vs. 1.75 in smaller studies). MGC exhibited larger tumors, advanced T stage, and higher risks of metastasis (lymphatic, vascular, peritoneal). CONCLUSION: MGC’s prognostic impact depends on tumor stage and cofactors, not histology alone. Clinical assessment should integrate TNM stage and tumor characteristics, particularly in early-stage disease.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.021 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".