Neutrophil to lymphocyte ratio and response to tyrosine kinase inhibitor therapy in metastatic renal cell carcinoma.
Bibliographic record
Abstract
477 Background: Neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) are markers of host inflammation and have prognostic value in many solid tumors. Here we aimed to explore the association of NLR and PLR with response to tyrosine kinase inhibitor (TKI) treatment in metastatic renal cell carcinoma (mRCC). Methods: Data from patients with mRCC treated at the Princess Margaret Cancer Centrein Toronto with a TKI as first-line treatment were retrospectively collected. The association of several variables with response to treatment (complete response [CR] or partial response [PR] vs. stable disease > 3 months [SD] or progressive disease [PD]) was assessed by binary logistic regression. Significant variables were dichotomized and cut-offs selected by the area under the receiver operating characteristic (AUC) curve. Results: Data from 157 patients treated between 11/2004 and 09/2012 were analyzed. Median age at start of TKI treatment was 61 years and first-line treatment was sunitinib, sorafenib, and other in 49%, 43%, and 8% of patients, respectively. Best response was CR/PR, SD, and PD in 27%, 55%, and 18% patients. On multivariable analysis NLR > 2.5 and Karnofsky Performance Status (KPS) < 90% were associated with a lower likelihood of response and each allocated a score of 1 unit. Response rates for a score of 0, 1, or 2 were 45% (29-61%), 28% (17-38%), 10% (1-19%), respectively. PLR did not retain association with response in multivariable analysis. Conclusions: NLR and KPS are associated with response to TKI treatment in mRCC. Data from an external validation set will also be presented.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.002 |
| 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 | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".