Evaluation of the prognostic value of derived neutrophil/lymphocyte ratio in early stage non-small cell lung cancer patients treated with stereotactic ablative radiotherapy
Bibliographic record
Abstract
The derived neutrophil-lymphocyte ration (dNLR) is a systemic inflammatory marker.The present study focusing on the prognostic value of pre-treatment dNLR in patients of early stage non-small cell lung cancer (NSCLC).From 2012 to 2016, patients with newly diagnosed early stage NSCLC were investigated. Only these who treated with stereotactic ablative radiotherapy (SABR) were enrolled in this study. dNLR was calculated from complete blood count prior to SABR. The optimal cut-off value of dNLR was determined by receiver operating curve. Kaplan-Meier curves and Cox proportional models were used to analyze the impact of pre-treatment dNLR on disease free survival (DFS) and overall survival (OS).There were 69 patients eligible for analysis, the median follow-up period was 30.9 months. Calculated by receiver operating characteristic curves, the optimal cut off value of dNLR was 1.99. Kaplan-Meier curves demonstrated that a decreased dNLR was correlated with favorable DFS and OS. In univariate analysis, high dNLR was associated with decreased survival; moreover, multivariate analysis revealed that a decreased dNLR was an independent significant favorable prognostic factor for both DFS and OS.An elevated pre-treatment dNLR may be an independent prognostic biomarker for DFS and OS in patients with early stage NSCLC that are eligible for SABR. dNLR is a reliable, inexpensive, simple, and readily available tool for risk-stratification and should be considered in daily clinical practice.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".