Neutrophil to lymphocyte ratio (NLR) and its effect on the prognostic value of the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) model for patients treated with targeted therapy (TT).
Bibliographic record
Abstract
470 Background: The neutrophil to lymphocyte ratio (NLR) is a marker of host inflammation and appears to have prognostic value in many solid tumors. We have found in a pilot RCC study that a NLR > 2.5 was predictive of a lower likelihood of response to TT on a multivariable analysis. Here we aim to explore the added value of the NLR to improve the prognostic value of the established IMDC criteria (Heng et al JCO 2009). Methods: We included patients from 7 consortium sites where NLR data was available for patients treated with TT and compared NLR cutoff <= 2.5 vs. >2.5 (i.e. low vs. high NLR) and adjusted using proportional hazards regression for the known poor prognostic criteria (listed in Table). Results: Data from 859 patients were included. NLR values were: Mean 4.98, Median 3.51, Mode 2.5, 95%CI 1.42 – 14.0. Using Cutoff <=2.5 vs. >2.5 Median overall survival (OS) is 30.4 months (95%CI 24.9-37.0, n= 237) vs. 15.7 months (95%CI 13.0-17.2, n=622); log-rank p value <0.0001. If we adjust for all six IMDC poor prognosis criteria in a proportional hazards regression model: HR of death for high NLR is 1.506 (1.177-1.928) p=0.0011, demonstrating NLR is still an independent predictor of poor OS even after using IMDC criteria. Conclusions: The NLR is a simple clinical value and is independently associated with poor overall survival even after adjustment for IMDC factors, including neutrophilia. The updated data set will be presented. [Table: see text]
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".