Unraveling the Predictive Value of the Novel Global Immune-Nutrition-Inflammation Index (GINI) on Survival Outcomes in Patients with Grade 4 Adult-Type Diffuse Gliomas
Bibliographic record
Abstract
Background: This investigation evaluated the predictive and prognostic efficacy of the newly developed global immune-nutrition-inflammation index (GINI) in patients with grade 4 adult-type diffuse gliomas, comparing it with other established indices such as the systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and pan-immune-inflammation value (PIV). Method: A retrospective cohort included 198 patients diagnosed with isocitrate dehydrogenase (IDH)-mutant gr4 (grade 4) astrocytoma and IDH-wt (wilde-type) glioblastoma (GBM) gr4 treated with surgical resection, radiotherapy, and temozolomide. Patients were stratified into two groups based on their GINI values: low GINI (<5815) and high GINI (≥5815). The primary endpoint was overall survival (OS). Results: High GINI was significantly associated with older age, poor performance status, multifocal tumors, and higher SII, SIRI, and PIV values (p < 0.005). The GINI demonstrated strong correlations with SII (r = 0.694), SIRI (r = 0.516), and PIV (r = 0.657) (p < 0.001). Patients with high GINI exhibited poorer OS (5.0 vs. 17.0 months) and PFS (5.0 vs. 13.0 months) in comparison to those with low GINI. Kaplan–Meier survival analysis revealed significantly prolonged OS and PFS among patients with low GINI (p < 0.001). Multivariate analysis identified high GINI as an independent negative risk factor for both PFS and OS. Conclusions: GINI is a robust predictor of clinical outcomes in IDH-mutant gr4 astrocytoma and IDH-wt GBM gr4, highlighting the crucial impact of nutrition and cancer cachexia. It shows superior prognostic value relative to the SII, SIRI, and PIV.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".