Association between neutrophil-to-eosinophil ratio (NER) and efficacy outcomes in the JAVELIN Renal 101 study.
Bibliographic record
Abstract
4549 Background: Baseline NER has been reported to be associated with outcomes of immuno-oncology based combination treatment in advanced renal cell carcinoma (aRCC). We report outcomes by baseline NER of patients with aRCC in the JAVELIN Renal 101 trial who received avelumab + axitinib (A + Ax) or sunitinib (S). Methods: We calculated the median NER (mNER) for patients in the A + Ax and the S arms at the data cutoff (April 20, 2020) for the 3rd interim analysis (IA3). Progression-free survival (PFS), overall survival (OS), and objective response (OR) by NER are reported. Multivariate Cox regression analyses of PFS and OS were also conducted. Results: At the IA3 cutoff date, the mNERs for the A + Ax arm (n = 383) and S arm (n = 396) were 29.2 and 27.0, respectively. OR, PFS and OS for both arms are summarized in the table below. Better observed treatment outcomes in OR (63.9% vs 55.2%) and median PFS (15.5 vs, 11.1 months) were observed for patients with a NER < median vs. NER ≥ median in the A + Ax arm, while there were not major differences in outcome based on NER in the S arm. The stratified hazard ratio (HR) for PFS in patients with a NER < median compared with those with a NER ≥ median in the A + Ax arm was 0.81 (95% CI, 0.630-1.035) and 0.93 (95% CI, 0.728-1.181) in the S arm. Patients with a NER < median had improved OS compared with those with a NER ≥ median in the A + Ax arm (stratified HR, 0.67; 95% CI, 0.481-0.940) and the S arm (stratified HR, 0.57; 95% CI, 0.424-0.779). Multivariate analysis showed that a low NER was associated with longer PFS and OS by treating baseline NER as either a continuous variable or a binary variable (dichotomized by median). Conclusions: Baseline NER may be predictive of OR and PFS in aRCC patients treated with A + Ax, and prognostic for overall survival regardless of therapy. Clinical trial information: NCT02684006. [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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".