Lipid Profiles Impact on the Oncologic Outcome of Upper Tract Urothelial Carcinoma
Bibliographic record
Abstract
Background: The prognosis of upper tract urothelial carcinoma (UTUC) varies, with T3/T4 UTUC having less than 50% 5-year survival post-radical nephroureterectomy (RNU). Lipid profiles including cholesterol (CHOL), low-density lipoprotein (LDL), and triglycerides (TGs), and high-density lipoprotein (HDL) have shown correlations with oncologic outcomes in various cancers. We aimed to investigate the prognostic significance of the lipid profiles in UTUC patients who had received RNU. Methods: In this retrospective study, a total of 217 UTUC patients who underwent RNU were analyzed. Prognostic factors for overall survival (OS), cancer-specific survival (CSS), and progression-free survival (PFS) were assessed using Cox proportional hazards regression model and competing risk analysis. Results: The median follow-up duration was 2.36 years. Fifty-one (23.50%) of the patients experienced tumor progression, 16 (7.37%) died from UTUC, and 41 (18.89%) died from all causes during the follow-up period. Multivariate analysis revealed that elevated CHOL, low HDL, and elevated TG were linked to worse OS (P = 0.0188, 0.0002, and 0.0001, respectively). Higher CHOL, LDL, and TG, as well as lower HDL significantly affected PFS (P < 0.001 for all), and elevated CHOL and TG were associated with poorer CSS (P = 0.0033 and 0.0179). A competing risk model indicated that elevated LDL increased the risk of cancer progression (P = 0.407), with CHOL increasing the risk of UTUC-specific mortality (P = 0.0162). Limitations include retrospective design, limited, single-time sampling and relatively small sample size. Conclusions: Lipid profiles were identified as prognostic indicators for UTUC patients post-RNU. It highlights the potential importance of lipid management in improving tumor-related outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".