The Prognostic Values of a Novel Preoperative Inflammation-Based Score in Japanese Patients With Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Background: Several previous researchers have investigated the prognostic value of the combinations of systemic inflammatory markers. However, the prognostic power of these systemic inflammatory markers is not identical. We aimed to establish a novel prognostic score based on systemic inflammatory markers. Methods: Four hundred non-small cell lung cancer (NSCLC) patients who underwent surgery and were followed more than 5 years were included. Univariate and multivariate analyses were calculated by the Cox proportional hazards regression model. Results: Among systemic inflammatory markers which were used for the previously reported indexes, preoperative serum C-reactive protein (CRP) and body mass index (BMI) were independent prognostic markers in multivariate analysis, while serum albumin level, neutrophil to lymphocyte ratio and platelet to lymphocyte ratio were not. Based on this result, a novel score was established. Patients with both normal CRP (< 0.13 ng/dL) and high BMI (> 20.6 kg/m 2 ) were allocated a score of 0. Patients in whom only one of these abnormalities was present were allocated a score of 1, whilst those with both high CRP and low BMI were given a score of 2. Patients with score 0 had 84.44% of 5-year cancer-specific survival, while patients with score 1 - 2 had a 61.88%. On multivariate analysis, this novel score was an independent prognostic factor. Conclusion: This novel score based on CRP and BMI might serve as an efficient prognostic indicator in resected NSCLC. World J Oncol. 2019;10(4-5):176-180 doi: https://doi.org/10.14740/wjon1222 Â
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".