Prognostic model using postoperative normalization of C-reactive protein levels in patients with upper tract urothelial carcinoma treated with radical nephroureterectomy
Bibliographic record
Abstract
INTRODUCTION: To improve the prediction of outcomes in patients who will undergo radical nephroureterectomy (RN U) for upper tract urothelial carcinoma (UTUC), we investigated the preoperative prognostic factors and developed a risk classification model. METHODS: A total of 144 patients who underwent RNU with history of neither neoadjuvant nor adjuvant chemotherapy between 2008 and 2022 were retrospectively reviewed. Associations between perioperative/clinicopathologic factors and outcomes, including cancer-specific survival (CSS), were assessed. We specifically focused on preoperative serum C-reactive protein (CRP) and its postoperative normalization. RESULTS: Non-normalization of postoperative serum CRP level and pathologic T3 stage were identified as independent predictive factors of shorter CSS in univariate and multivariate analysis (p=0.0150 and 0.0037, hazard ratio: 3.628 and 4.470, respectively). We classified the patients into three groups using these factors and found that five-year CSS was 88%, 42.5%, and 0% in the low-risk group (zero factors), intermediate-risk group (one factor), and high-risk group (two factors), respectively (p<0.0001). CONCLUSIONS: Non-normalization of postoperative serum CRP level and pathologic T stage were identified as independent postoperative prognostic factors in patients with UTUC who underwent RNU. These factors can stratify three prognostic groups and may help urologists in clinical decision-making for adjuvant therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".