Overall survival by baseline and on-treatment systemic immune-inflammation index in patients with advanced cancer receiving immune checkpoint inhibitors: a large single-centre cohort study
Bibliographic record
Abstract
Abstract The Systemic Immune-Inflammation Index (SIII; neutrophils/lymphocytes × platelets) is a low-cost biomarker proposed to predict outcomes with immune checkpoint inhibitors (ICIs). This study evaluated associations of baseline and early on-treatment changes in SIII with overall survival (OS) for common ICI regimens. Patients with advanced cancer treated with ICIs at a UK centre were categorized by baseline SIII (above vs. below the median) and by changes at 3–6 weeks (increase/decrease). OS was analysed using Kaplan–Meier estimates. Adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) were calculated using multivariable Cox regression. Among 2578 patients included, 1514 deaths occurred over a median follow-up of 2.6 years. Common regimens included pembrolizumab or atezolizumab with (15.9%) or without chemotherapy (13.9%) for NSCLC, and nivolumab plus ipilimumab for melanoma (12.6%). Lower baseline SIII was associated with improved OS (28.1 vs. 11.1 months; aHR 0.56, 0.50–0.62), with a weaker association observed in those receiving ICI-targeted therapy combinations. An on-treatment increase in SIII was linked to reduced OS (16.8 vs. 21.5 months; aHR 1.33, 1.18–1.49). Patients with low baseline SIII and an on-treatment decline had the longest OS (33.2 months), whereas those with high baseline SIII and an on-treatment increase had the shortest (8.2 months; aHR 2.88, 2.41–3.44; interaction between baseline and on-treatment SIII P < 0.001). SIII is a low-cost, readily available biomarker. Both baseline SIII levels and on-treatment changes in SIII are significantly associated with OS. SIII may help identify patients who could benefit from closer monitoring or treatment adjustments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".