Clinical value of cholinesterase in patients treated with radical nephroureterectomy for upper urinary tract carcinoma
Bibliographic record
Abstract
PURPOSE: To evaluate the prognostic value and the clinical impact of preoperative serum cholinesterase (ChoE) levels on decision-making in patients treated with radical nephroureterectomy (RNU) for clinically non-metastatic upper tract urothelial cancer (UTUC). METHODS: A retrospective review of an established multi-institutional UTUC database was performed. We evaluated preoperative ChoE as a continuous and dichotomized variable using a visual assessment of the functional form of the association of ChoE with cancer-specific survival (CSS). We used univariable and multivariable Cox regression models to establish its association with recurrence-free survival (RFS), CSS, and overall survival (OS). Discrimination was evaluated using Harrell's concordance index. Decision curve analysis (DCA) was used to assess the impact on clinical decision-making of preoperative ChoE. RESULTS: A total of 748 patients were available for analysis. Within a median follow-up of 34 months (IQR 15-64), 191 patients experienced disease recurrence, and 257 died, with 165 dying of UTUC. The optimal ChoE cutoff identified was 5.8 U/l. ChoE as continuous variable was significantly associated with RFS (p < 0.001), OS (p < 0.001), and CSS (p < 0.001) on univariable and multivariable analyses. The concordance index improved by 8%, 4.4%, and 7% for RFS, OS, and CSS, respectively. On DCA, including ChoE did not improve the net benefit of standard prognostic models. CONCLUSION: Despite its independent association with RFS, OS, and CSS, preoperative serum ChoE has no impact on clinical decision-making. In future studies, ChoE should be investigated as part of the tumor microenvironment and assessed as part of predictive and prognostic models, specifically in the setting of immune checkpoint-inhibitor 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 | 0.006 |
| 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".