Change in neutrophil to lymphocyte ratio as a prognostic and predictive marker in response to targeted therapy for metastatic renal cell carcinoma.
Bibliographic record
Abstract
404 Background: The neutrophil to lymphocyte ratio (NLR) is a marker of inflammation. We evaluated whether NLR is independently prognostic when adjusted for the International mRCC Database Consortium (IMDC) model and evaluated change in NLR ("NLR conversion") as a predictive marker of response to targeted therapy. Methods: A total of 5,227 metastatic renal cell carcinoma (mRCC) patients treated with targeted therapy were included; 1,199 patients in the training cohort from the IMDC and 4028 patients as the validation cohort from pooled prospective randomized controlled trials involving targeted therapy. NLR was examined at initiation of first-line targeted therapy and at 6 weeks after. The prognostic role of NLR and NLR conversion on overall survival (OS) and progression free survival (PFS) was assessed using Cox regression models adjusting for IMDC prognostic score. Results: Median baseline NLR was 3.4 and 2.9 in the training and validation cohorts, respectively. NLR >3.0 at baseline was independently associated with OS and PFS in both the training and validation cohorts (Table). A decrease in NLR by week 6 was associated with longer OS (21.1 vs. 9.7 months; HR 0.57, p<0.001), PFS (8.8 vs. 4.6 months; HR 0.54, p<0.001), and higher objective response rates (35% vs. 13%, p<0.001) compared to patients without a decrease. A rise in NLR showed opposite effects for all three endpoints. These findings were also confirmed in the validation set. Conclusions: NLR is an independent prognostic factor after controlling for IMDC criteria. NLR conversion can be an early biomarker of benefit to targeted therapy. [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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".