The Prognostic Role of the Change in Neutrophil-to-Lymphocyte Ratio During Neoadjuvant Chemotherapy in Patients with Muscle-Invasive Bladder Cancer: A Retrospective, Multi-Institutional Study
Bibliographic record
Abstract
Background: The impact of the change in the neutrophil-to-lymphocyte ratio (NLR) during neoadjuvant chemotherapy (NAC) on outcomes in patients with muscle-invasive bladder cancer (MIBC) is poorly understood. Objective: To evaluate the prognostic impact of the change in NLR during NAC for patients with MIBC. Methods: Patients referred to academic, community, and quaternary referral centres in Alberta, Canada from 2005 to 2015, Ontario, Canada from 2005 to 2013, and Southampton, UK from 2004 to 2010 were evaluated. 376 eligible patients were treated with NAC for clinical T2-4aN0M0 disease, and 296 were evaluable for the change in NLR. A high NLR was defined as being an NLR > 3. Relationships between the change in NLR from baseline to mid-NAC (pre-cycle 3) and outcomes were analyzed using multivariable Cox regression. Kaplan-Meier analysis was used with the log-rank test for group comparisons. Results: Median follow-up was 22.0 months (95% confidence interval [CI]: 14.9–30.0). Patients with a sustained high NLR had a median disease-free survival (DFS) of 12.6 months, compared to 34.8 months for those with a sustained low NLR (log-rank test p = 0.0025; hazard ratio [HR] 0.61 [95% CI: 0.44–0.84]). Median overall survival (OS) was 19.4 months for patients with a sustained high NLR, compared to 44.0 months for patients with a sustained low NLR (log-rank test p = 0.0011; HR 0.54 [95% CI: 0.38–0.77]). Conclusions: A sustained high NLR from baseline to mid-NAC is an independent prognostic factor for patients with MIBC.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".