Inflammatory Ratios as Predictors for Tumor Invasiveness, Metastasis, Resectability and Early Postoperative Evolution in Gastric Cancer
Bibliographic record
Abstract
Our study aimed to evaluate the baseline neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII) in relation to invasion, metastasis, and resectability for patients with gastric cancer, respectively, as predictors of death during hospitalization or surgical complications. A retrospective cohort study was conducted on 657 gastric cancer subjects. Inflammatory biomarkers were computed. The associations with tumor stage, metastasis, optimal procedure, in-hospital mortality, and surgical complications were evaluated. Subjects who underwent curative-intent surgery presented lower median NLRs (2.9 vs. 3.79), PLRs (166.15 vs. 196.76), and SIIs (783.61 vs. 1122.25), and higher LMRs (3.34 vs. 2.9) than those who underwent palliative surgery. Significantly higher NLRs (3.3 vs. 2.64), PLRs (179.68 vs. 141.83), and SIIs (920.01 vs. 612.93) were observed for those with T3- and T4-stage cancer, in comparison with those with T1- and T2-stage cancer. Values were significantly higher in the case of metastasis for the NLR (3.96 vs. 2.93), PLR (205.22 vs. 167.17), and SII (1179 vs. 788.37) and significantly lower for the LMR (2.74 vs. 3.35). After the intervention, the NLR, PLR, and SII values were higher (p < 0.01) for patients with surgical complications, and the NLR and SII values were higher for those who died during hospitalization. Higher NLRs, PLRs, SIIs, and lower LMRs were associated with a more aggressive tumor; during early follow-up, these were related to post-operative complications and death during hospitalization.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".