Potential of neutrophil-to-eosinophil ratio as a new prognostic tool for patients with advanced renal cell carcinoma receiving first-line immuno-oncology combinations
Bibliographic record
Abstract
Systemic therapies for advanced renal cell carcinoma (aRCC) have evolved dramatically since the approvals of immuno-oncology (IO) agents.1 Thereafter, risk-based decision-making has become increasingly important to guide treatment selection. To date, there have been two widely-used nomograms for risk stratification of patients with aRCC: the Memorial Sloan Kettering Cancer Center (MSKCC) model2 and the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) model.3 Both nomograms consist of similar clinical parameters such as short time from diagnosis to systemic therapy, poor performance status, low haemoglobin and high corrected calcium. In addition to these risk factors, the MSKCC model contains high lactate dehydrogenase (LDH), whereas the IMDC model contains neutrophils and platelets instead of LDH. In the era of IO combinations, several additional risk factors have been advocated including inflammatory markers such as neutrophil-to-lymphocyte ratio,4 C-reactive protein5 and absolute lymphocyte count,6 given that these biomarkers are closely linked to how IO agents use host immunity during antitumour immune response against aRCC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".