Real world outcomes in advanced urothelial cancer and the role of neutrophil to lymphocyte ratio.
Bibliographic record
Abstract
e16020 Background: Advanced urothelial carcinoma (UC) patients have a poor prognosis. In the first and second line UC treatment setting, we investigated real world outcomes and evaluated the prognostic role of the neutrophil to lymphocyte ratio (NLR). Methods: A retrospective analysis was performed on advanced UC patients treated with systemic therapy. Overall response rates (ORR), time to treatment failure (TTF) and overall survival (OS) were calculated. Cox regression analysis was performed to examine the association between baseline NLR (low NLR<3 vs high NLR≥3) and TTF and OS. Results: We evaluated 233 advanced UC patients. In the first line setting, the ORR was 25%. Median TTF and OS were 6.9 mo and 9 mo, respectively. Low baseline NLR was significantly associated with improved 8.3 mo median TTF, versus 5.8 mo for high NLR patients (p=0.05). Low NLR was significantly correlated with a longer median OS of 13.1 mo, in comparison to 8.2 mo in patients with high NLR (p=0.007). In the second line, an ORR of 22%, a median TTF of 4.1 mo and a median OS of 8 mo were observed. Low NLR in the second line was significantly associated with improved median TTF at 7.9 mo, versus 3.6 mo for patients with high NLR (p=0.03). Second line low NLR was also significantly associated with a longer median OS of 12.2 mo, in comparison to 6.8 mo in patients with high NLR (p=0.003). Conclusions: In this real world analysis of advanced UC patients, first line outcomes were lower than expected, while response rates in the second line compared favorably to the literature, suggesting a highly selected patient population actually receives second line treatment. A low baseline NLR in the first and second line is associated with improved TTF and OS and warrants further prospective evaluation. [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 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".