Prognostic Role of Neutrophil to Lymphocyte Ratio in Ovarian Cancer: A Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to investigate the prognostic role of neutrophil to lymphocyte ratio in ovarian cancer. Growing number of articles reported the relationship between neutrophil to lymphocyte ratio and prognosis in ovarian cancer, but the results remains inconclusive. The meta-analysis was conducted to analyze the association of pretreatment neutrophil to lymphocyte ratio with overall survival and progression-free survival. METHODS: We performed a systematic literature research of PubMed, EMBASE, Medline, and Cochrane library for relevant studies up to October 8, 2017. The quality of included studies was assessed by the Newcastle-Ottawa Quality Assessment Scale. The hazard ratio and corresponding 95% confidence intervals were calculated. We checked the heterogeneity by the Q test and Higgins I-squared statistic. Begg funnel plot and Egger linear regression test were also applied for ascertain publication bias. All of the statistical analyses were performed using STATA version 12.0. RESULTS: A total of 12 studies with 4046 patients were included in our study. The results indicated that depressed neutrophil to lymphocyte ratio was significantly correlated with higher overall survival (hazard ratio = 1.409, 95% confidence intervals = 1.112-1.786, P = .005) and progression-free survival (hazard ratio = 1.523, 95% confidence intervals = 1.187-1.955, P = .001) in ovarian cancer. Subgroup analysis by ethnicity of overall survival and progression-free survival showed that the prognostic effect of neutrophil to lymphocyte ratio was found both in Asians and Caucasians. CONCLUSION: Patients with depressed neutrophil to lymphocyte ratio had a higher overall survival and progression-free survival in ovarian cancer. This meta-analysis provided neutrophil to lymphocyte ratio as an available predictor of overall survival and progression-free survival for patients with ovarian cancer.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.047 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".