Comprehensive Evaluation of Inflammatory Biomarkers in Cervical Cancer Treated with Chemoradiotherapy
Bibliographic record
Abstract
Objective: Inflammatory biomarkers have been shown to possess both prognostic and predictive significance in various cancers. Among the emerging biomarkers, the pan-immune-inflammation value (PIV) has recently been introduced as a novel indicator representing both the immune response and the systemic inflammatory state. This study aims to comprehensively evaluate the predictive value of inflammatory biomarkers on survival outcomes in cervical cancer patients undergoing chemoradiotherapy. Methods: A total of 90 patients who had undergone chemoradiotherapy for cervical cancer were included. Data on demographics, treatment protocols, pre-treatment blood parameters, and survival outcomes were collected. The association between inflammatory biomarkers and survival outcomes was investigated through univariate and multivariate analyses. Results: The univariate analysis identified the following as predictors of progression-free survival (PFS): neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), monocyte–lymphocyte ratio (MLR), systemic immune-inflammation index (SII), PIV, C-reactive protein (CRP), albumin, and tumor size. Multivariate analysis revealed that only the PIV significantly predicted PFS (HR 3.05, 95% CI 1.0 to 9.3, p = 0.04). In the univariate analysis, several variables were predictive of overall survival (OS), including NLR, PLR, MLR, SII, PIV, CRP, LDH, albumin, tumor size, and Eastern Cooperative Oncology Group Performance Status (ECOG PS). Multivariate analysis revealed CRP (HR 3.41, 95% CI 1.5 to 7.7, p = 0.003) and ECOG PS (HR 4.78, 95% CI 1.3 to 17.3, p = 0.01) predictive of OS, with PIV approaching statistical significance (HR 2.56, 95% CI 0.8 to 7.6, p = 0.09). Conclusions: This study provides the first comprehensive analysis of the association between cervical cancer and various inflammatory biomarkers. Many of these biomarkers have demonstrated predictive value for survival outcomes in patients with cervical cancer undergoing definitive chemoradiotherapy. Among the biomarkers evaluated, CRP and PIV were identified as the most predictive, warranting further exploration in future research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".