Comparison of Inflammation-Based Prognostic Scores in Patients undergoing Curative Resection for Non-small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Inflammation-based prognostic scores, including Glasgow prognostic score (GPS), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), prognostic nutritional index (PNI), systemic immune-inflammation index (SII) and advance lung cancer inflammation index (ALI) are reported to be associated with survival in patients with non-small cell lung cancer (NSCLC). However, at present, there are no studies that compared these scoring systems for resectable NSCLC. METHODS: Three hundred forty-one NSCLC patients who underwent surgery at our institution were included. The optimal cut-off values of SII and ALI were calculated by the Cutoff Finder. The area under the receiver operating characteristics curve (AUC) was calculated to compare the predictive ability of each of the scoring systems. Univariate and multivariate analyses were performed to identify the clinicopathological variables associated with overall survival. RESULTS: /L and 37.66, respectively. All scores were significantly related to the 5-year cancer-specific survival. The ALI consistently had a higher AUC value in comparison with other inflammation-based prognostic scores. A multivariate analysis showed that GPS and ALI were independently associated with overall cancer-specific survival. CONCLUSION: To the best of our knowledge, this is the first study to demonstrate that GPS and ALI appear to be superior to other inflammation-based prognostic scores in patients undergoing potentially curative resection for NSCLC.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".