Modified Glasgow Prognostic Score as a Marker for Predicting Outcomes in Patients with either Bladder or Prostate Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to investigate the association of pre-treatment-modified Glasgow Prognostic Score (mGPS) with survival-related outcomes in patients with bladder cancer (BC) and prostate cancer (PC). METHODS: A systematic search was performed in PubMed, EMBASE, Web of Science, and Scopus databases for cohort studies in adult participants (≥18 years). The exposure was pre-treatment mGPS, and the outcomes of interest were overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS). Eligible studies compared low mGPS (considered as a score of 0) with a score of ≥1. A random-effects model was used for the analysis. Pooled effect sizes were reported as hazard ratio (HR) with 95% confidence intervals (CIs). Subgroup analysis was performed based on the tumour stage (≤T2 and >T2), sample size (≥200 and <200), treatment (surgical and non-surgical), and the Newcastle-Ottawa Scale (NOS) score (≥8 and ≤7). RESULTS: Of 20 studies included in the analysis, 19 studies were retrospective cohort studies. Fourteen studies reported data of patients with BC, and the remaining 6 studies focused on PC patients. Compared to mGPS of 0, higher scores were associated with reduced OS (HR: 2.65; 95% CI: 1.99, 3.52), CSS (HR: 1.64; 95% CI: 1.19, 2.26), and RFS (HR: 1.77; 95% CI: 1.50, 2.08). There was no evidence of publication bias (Egger's p > 0.05). These associations remained valid in subgroup analysis. CONCLUSION: Higher mGPS values were found to be associated with significantly reduced survival outcomes. These findings underscore the prognostic significance of mGPS, thereby highlighting its potential clinical utility in risk stratification and treatment decision-making.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".