The role of neutrophil-lymphocyte ratio as a prognostic indicator in patients undergoing nephrectomy for renal cell carcinoma
Bibliographic record
Abstract
INTRODUCTION: Prognosis in patients with cancer is influenced by underlying tumour biology and also the host inflammatory response to the disease. There is limited evidence to suggest that an elevated neutrophil-lymphocyte ratio (NLR) predicts a poorer prognosis in patients undergoing nephrectomy for renal cell carcinoma (RCC). The aim of this paper is to investigate if patients undergoing nephrectomy for RCC with NLR ≤4 have a better overall and recurrence-free survival than patients with NLR >4. METHODS: All patients who underwent nephrectomy at a single centre between January 1, 2011 and December 31, 2014 were identified. Patients were included if postoperative histology demonstrated RCC and if preoperative NLR was available. Patients were excluded if nephrectomy was not curative intent (i.e., cytoreductive nephrectomy), if primary tumour was graded to be T3b-4 disease, if there was presence of nodal or metastatic disease on preoperative staging, or if adequate followup notes were not available. Primary and secondary outcomes were overall survival and recurrence-free survival, respectively. RESULTS: A total of 154 patients were included in analysis of overall survival; 146 patients were included in analysis of recurrence-free survival. Patients with NLR ≤4 had a much better overall survival than patients with NLR >4 (95% vs. 78%; p=0.0219). Patients with NLR >4 also had higher rates of recurrence (p=0.0218). CONCLUSIONS: NLR may be a useful tool in identifying patients who may benefit from more frequent surveillance in the early postoperative period and may allow clinicians to offer surveillance schemes tailored to the individual patient.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".