Prognostic and clinical significance of modified glasgow prognostic score in pancreatic cancer: a meta-analysis of 4,629 patients
Bibliographic record
Abstract
In this study, we evaluated the association of modified Glasgow Prognostic Score (mGPS) with prognosis in pancreatic cancer (PC) by performing a meta-analysis. Potentially eligible studies were shortlisted by searching PubMed, Embase, Web of Science, Scopus, and the Cochrane Library. A total of 4,629 patients with PC from 25 studies were finally included in this meta-analysis. Meta-analyses were performed using a random-effects model or fixed-effect model according to heterogeneity. We pooled the hazard ratios (HRs) with 95% confidence intervals (CIs) to estimate the association between mGPS and overall survival (OS). The results showed that elevated mGPS correlated with poor OS in patients with PC (HR=1.92, 95% CI=1.60-2.30, p<0.002). In addition, subgroup analysis indicated that increased mGPS remained a significant prognostic factor irrespective of the study design, region, disease status, treatment, survival analysis, cancer type, study center, or the Newcastle-Ottawa Scale (NOS) score (all p<0.05). There was a significant correlation between higher mGPS and male gender (Odds ratio [OR]=1.30, 95% CI=1.01-1.67, p=0.038). Elevated pretreatment mGPS is a marker of poor prognosis in patients with PC. As an easily available and cost-effective inflammatory parameter, mGPS can serve as a promising tool for prognostication in PC.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.047 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".