Validation of the Scottish Inflammatory Prognostic Score (SIPS) in NSCLC Patients Treated with First-Line Pembrolizumab
Bibliographic record
Abstract
BACKGROUND: /L), has been identified as a prognostic biomarker for patients with non-small cell lung cancer (NSCLC) undergoing treatment with pembrolizumab monotherapy. We sought to validate this biomarker of systemic inflammation in an external cohort. METHODS: Patients treated with first-line pembrolizumab for advanced NSCLC with programmed death-ligand 1 (PD-L1) expression ≥ 50% at an English cancer centre were identified. Pre-treatment clinicopathological characteristics and the SIPS were recorded. The relationship between these and progression-free survival (PFS) and overall survival (OS) was examined. RESULTS: = 20) as SIPS 2. Factors such as age, performance status (PS) and brain metastases presence were significantly correlated with SIPS categories. Multivariate analysis revealed that both SIPS and PD-L1 status were independently associated with PFS and OS. The combination of SIPS with either PS or PD-L1 expression enhanced the ability to detect patients with the most favourable or poorest survival. CONCLUSIONS: Our study confirms the prognostic significance of the SIPS in patients with advanced NSCLC treated with pembrolizumab in the context of high PD-L1 expression. SIPS offers a straightforward, clinically applicable approach to patient stratification, potentially guiding therapeutic decisions and enhancing outcomes in advanced NSCLC. Future research should focus on validating these findings in prospective studies and exploring the integration of SIPS into clinical practice, alongside other prognostic markers, to optimize treatment strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".