Predictive Value of GINI and ALBI Grades in Esophageal Cancer Receiving Chemoradiotherapy
Bibliographic record
Abstract
Objectives: The principal objective of this study was to assess the predictive efficacy of the global immune–nutrition–inflammation index (GINI) and the albumin–bilirubin (ALBI) score among patients receiving chemoradiotherapy for esophageal cancer. Methods: A retrospective analysis was conducted on 46 patients who received definitive or neoadjuvant radiotherapy for esophageal cancer at our institution. Blood samples were collected from these patients prior to the initiation of radiotherapy to measure the biomarkers, including the C-reactive protein (CRP), neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), monocyte–lymphocyte ratio (MLR), the global immune–nutrition–inflammation index (GINI), and the albumin–bilirubin (ALBI) grade. The predictive significance of these biomarkers for progression-free survival (PFS) and overall survival (OS) was evaluated using both univariate and multivariate Cox regression analyses. Results: The median follow-up time for this study was 19.5 months (range: 2.6–166.3 months). Univariate analysis revealed that the platelet count (p = 0.003) and monocyte count (p = 0.04) were significant predictors of PFS. In the multivariate analysis, only the platelet count (p = 0.005) remained an independent predictor of PFS. Univariate analysis demonstrated that the neutrophil count (p = 0.04), lymphocyte count (p = 0.01), NLR (p = 0.005), PLR (p = 0.004), CRP (p = 0.02), ALBI grade (p = 0.01), and GINI (p = 0.005) were significant predictors of OS. Multivariate analysis identified the GINI as a predictor of OS, approaching statistical significance (p = 0.08). Conclusion: The results of our study indicate that the pretreatment GINI and ALBI grades are significantly and independently associated with the OS rates in patients with esophageal cancer who are undergoing chemoradiotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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".