Comparison of Four Clinical Prognostic Scores in Patients with Advanced Gastric and Esophageal Cancer
Bibliographic record
Abstract
BACKGROUND: Prognostic scores that can identify patients at risk for early death are needed to aid treatment decision-making and patient selection for clinical trials. We compared the accuracy of four scores to predict early death (within 90 days) and overall survival (OS) in patients with metastatic gastric and esophageal (GE) cancer. METHODS: Advanced GE cancer patients receiving first-line systemic therapy were included. Prognostic risks were calculated using: Royal Marsden Hospital (RMH), MD Anderson Cancer Centre (MDACC), Gustave Roussy Immune (GRIm-Score), and MD Anderson Immune Checkpoint Inhibitor (MDA-ICI) scores. Overall survival (OS) was estimated using the Kaplan-Meier method. Cox proportional hazards models were used to analyze associations between prognostic scores and OS. The predictive discrimination was estimated using Harrell's c-index. Predictive ability for early death was measured using time-dependent AUCs. RESULTS: In total, 451 patients with metastatic GE cancer were included. High risk patients had shorter OS for all scores (RMH high- vs. low-risk median OS 7.9 vs. 12.2 months, P < .001; MDACC 6.8 vs. 11.9 months P < .001; GRIm-Score 5.3 vs. 13 months, P < .001; MDA-ICI 8.2 vs. 12.2 months, P < .001). On multivariable analysis, each prognostic score was significantly associated with OS. The GRIm-Score had the highest predictive discrimination and predictive ability for early death. CONCLUSIONS: The GRIm-Score had the highest accuracy in predicting early death and OS. Clinicians may use this score to identify patients at higher risk of early death to guide treatment decisions including clinical trial enrolment. This score could also be used as a stratification factor in future clinical trial designs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".