Evaluation of neutrophil-to-lymphocyte ratio prior to prostate biopsy to predict biopsy histology: Results of 1836 patients
Bibliographic record
Abstract
INTRODUCTION: We evaluate the role of NLR prior to prostate biopsy to predict biopsy histology and Gleason score in patients with prostate cancer. METHODS: In this retrospective study, we evaluated data of patients underwent prostate biopsy between May 2005 and March 2015. We collected the following data: age, prostate-specific antigen (PSA), biopsy histology, Gleason score (GS) in prostate cancer patients, neutrophil counts, and lymphocyte counts. Patients were grouped as benign prostatic hyperplasia (BPH), prostate cancer, and prostatitis. The Chi square test was used to compare categorical variables and analysis of variance (ANOVA) was applied for continuous variables. RESULTS: Data of 1836 patients were investigated. The mean age, total PSA and neutrophil-lymphocyte ratio (NLR) of the population were 66.8 ± 8.17 years, 9.38 ± 4.7 ng/dL, and 3.11 ± 1.71, respectively. Patients were divided as follows: 625 in the group with BPH history, 600 in the prostatitis group, and 611 in the prostate cancer histology group. The mean NLR of the prostatitis group was higher compared to the prostate cancer and BPH groups (p = 0.0001). The mean NLR of the prostate cancer group was significantly higher compared to the BPH group (p = 0.002). The GS 8-10 group had a significantly higher mean NLR compared to GS 5-6 (3.64 vs. 2.54, p = 0.0001) and GS 7 (3.64 vs. 2.58, p = 0.0001) patients. CONCLUSIONS: NLR was found to differ with regard to histology of prostate biopsy and higher GS was associated with higher NLR in patients with prostate cancer. However prostatitis prevents the use of NLR in predicting prostate cancer before a prostate biopsy. Also, the retrospective nature and lack of multivariate analysis in this study somewhat limits the relevance of these results.
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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 | 0.001 | 0.001 |
| 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".