Partial vs. radical nephrectomy in non-metastatic pT3a kidney cancer patients: a population-based study
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to test for differences in cancer specific mortality (CSM) rates between radical nephrectomy (RN) and partial nephrectomy (PN) in pT3a nmRCC patients. METHODS: Within the surveillance, epidemiology, and end results database (2005-2016), 13,177 pT3a patients treated with either PN or RN were identified. Before and after 1:2 ratio propensity score (PS)-match between PN and RN patients, cumulative incidence plot and competing risks regression (CRR) were used to test differences in CSM and other cause mortality (OCM) rates. RESULTS: Relative to PN (N.=1615, 22.5%), RN patients harbored higher tumor size (72 vs. 38 mm; >70 mm 51 vs.10%), of more aggressive histology, collecting duct (0.4 vs. 0.2%) and sarcomatoid (2.3 vs.0.8%), of higher grade (51.0 vs. 37.5%). After PS-matching and OCM adjustment, 5-year CSM was 3-fold higher after RN than PN (P<0.01). Similarly, after PS matching and CSM adjustment, also 5-year OCM rates were higher after RN (HR: 1.59, P=0.0003). CONCLUSIONS: PN does not appear to compromise the oncological outcomes in patients with pT3a or high-grade renal masses when compared with RN. Therefore, these concerns should not deter a surgeon from attempting PN when otherwise technically feasible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".