Prognostic value of neutrophil to lymphocyte ratio for gastric cancer.
Bibliographic record
Abstract
BACKGROUND: Although the prognostic value of the neutrophil to lymphocyte ratio (NLR) in gastric cancer (GC) patients has been investigated by many studies, the results are heterogeneous. The objective of this systematic review is to ascertain the prognostic value of NLR in GC patients. METHODS: PubMed and Embase were retrieved to identify potential studies published before 8 June, 2014. Newcastle-Ottawa Scale (NOS) for cohort study was used to assess the quality of all eligible studies. RESULTS: Of the 20 studies included in this systematic review, 17 studies investigated the effect of NLR on overall survival (OS), 11 studies reported that NLR negatively affected OS in their multivariante analysis, and 16 studies reported that NLR negatively affected OS in univariate analysis. Three studies investigated the effect of NLR on progression-free survival (PFS), reporting that increased NLR was associated with worse PFS. Four studies investigated the effect of NLR on disease-free survival (DFS), two of which reported that increased NLR was associated with worse DFS. Two studies investigated the effect of NLR on disease special survival (DSS), but neither observed any significant association between NLR and DSS. The major design deficiencies of the studies available were retrospective data collection, inadequacy of follow-up cohorts, and unavailability of the method used for outcome assessment. CONCLUSIONS: Based on the above findings, we conclude that NLR may be a useful prognostic index (PI) for GC. In addition, future studies with prospective design, long-term follow-up and fully adjusted confounding factors are needed to rigorously assess the prognostic value of NLR for GC.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".