Identification of the best complete blood count-based predictors for bladder cancer outcomes in patients undergoing radical cystectomy
Bibliographic record
Abstract
BACKGROUND: We sought to determine which parsimonious combination of complete blood count (CBC)-based biomarkers most efficiently predicts oncologic outcomes in patients undergoing radical cystectomy (RC) for bladder cancer (BC). METHODS: Using our institutional RC database (1992-2012), nine CBC-based markers (including both absolute cell counts and ratios) were evaluated based on pre-treatment measurements. The outcome measures were recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Time-dependent receiver-operating characteristics curves were used to characterise each biomarker. The CBC-based biomarkers, along with several clinical predictors, were then considered for inclusion in predictive multivariable Cox models based on the Akaike Information Criterion. RESULTS: Our cohort included 418 patients. Neutrophil-lymphocyte ratio (NLR) was the only biomarker satisfying criteria for inclusion into all models, independently predicting RFS (HR per 1-log unit=1.52, 95% CI=1.17-1.98, P=0.002), CSS (HR=1.47, 95% CI=1.20-1.80, P<0.001), and OS (HR=1.56, 95% CI=1.16-2.10, P=0.004). Haemoglobin was also independently predictive of CSS (HR per 1 g/dl=0.91, 95% CI=0.86-0.95, P<0.001) and OS (HR=0.90, 95% CI=0.88-0.93, P<0.001), but not RFS. CONCLUSIONS: Among CBC biomarkers studied, NLR was the most efficient marker for predicting RFS, whereas NLR and haemoglobin were most efficient in predicting CSS and OS. NLR and haemoglobin are promising, cost-effective, independent biomarkers for predicting oncologic BC outcomes following RC. CONDENSED ABSTRACT: Various CBC-based biomarkers have separately been shown to be predictive of oncologic outcomes in patients undergoing cystectomy for BC. Our study evaluated these biomarkers, and determined that NLR is the best CBC-based biomarker for predicting RFS, whereas NLR and haemoglobin are most efficient for predicting CSS and OS.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".