Prognostic Value of Lymphocyte-to-Monocyte Ratio (LMR) in Patients With Prostate Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The objective of this study is to evaluate the prognostic value of lymphocyte-to-monocyte ratio (LMR) in patients with prostate cancer (PCa) by a method of meta-analysis. China National Knowledge Infrastructure (CNKI), Wanfang Data, PubMed, Web of Science, Cochrane Library, and Embase were searched to collect relevant literature until March 2023. The Newcastle-Ottawa Scale was used to assess the bias risk of the literature included. Hazard ratios (HRs) and 95% confidence intervals (CIs) were used to evaluate the prognostic value of LMR in PCa. Stata 15.0 statistical software was used for data analysis. A total of six published articles were included in this meta-analysis, containing 1,104 patients with PCa. The results of the meta-analysis indicated better overall survival (OS; HR = 1.73, 95% CI: 1.73, p = .001) and progression-free survival (PFS; HR = 2.63, 95% CI: 1.58~4.38, p < .001) in patients with PCa with low LMR compared with high LMR. In conclusion, compared with low LMR, PCa patients with high LMR have a better prognosis. LMR is an independent risk factor affecting the long-term prognosis of patients with PCa. The detection of LMR before treatment is of certain significance in judging the clinical prognosis of patients with PCa.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.047 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".