The role of adjuvant chemotherapy for lymph node‐positive upper tract urothelial carcinoma following radical nephroureterectomy: a retrospective study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of adjuvant chemotherapy (AC) on mortality after radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC) with positive lymph nodes (LNs) and to identify patient subgroups that are most likely to benefit from AC. PATIENTS AND METHODS: We retrospectively analysed data of 263 patients with LN-positive UTUC, who underwent full surgical resection. In all, 107 patients (41%) received three to six cycles of AC, while 156 (59.3%) were treated with RNU alone. UTUC-related mortality was evaluated using competing-risks regression models. RESULTS: In all patients (T(all) N+), administration of AC had no significant impact on UTUC-related mortality on univariable (P = 0.49) and multivariable (P = 0.11) analysis. Further stratified analyses showed that only N+ patients with pT3-4 disease benefited from AC. In this subgroup, AC reduced UTUC-related mortality by 34% (P = 0.019). The absolute difference in mortality was 10% after the first year and increased to 23% after 5 years. On multivariable analysis, administration of AC was associated with significantly reduced UTUC-related mortality (subhazard ratio 0.67, P = 0.022). Limitations of this study are the retrospective non-randomised design, selection bias, absence of a central pathological review and different AC protocols. CONCLUSIONS: AC seems to reduce mortality in patients with pT3-4 LN-positive UTUC after RNU. This subgroup of LN-positive patients could serve as target population for an AC prospective randomised trial.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".