The impact of lymph node dissection and positive lymph nodes on cancer‐specific mortality in contemporary <scp>pT</scp> <sub>2‐3</sub> non‐metastatic renal cell carcinoma treated with radical nephrectomy
Bibliographic record
Abstract
Objective To assess the effect of lymph node dissection ( LND ), number of removed nodes ( NRN ), and number of positive nodes ( NPN ), on cancer‐specific mortality ( CSM ) in contemporary vs historical patients with pT 2–3 N any M 0 renal cell carcinoma ( RCC ) treated with radical nephrectomy ( RN ). Patients and Methods Within the Surveillance, Epidemiology, and End Results database (2001–2013), we identified patients with non‐metastatic pT 2–3 N any RCC who underwent RN with or without LND . Kaplan–Meier analyses and multivariable Cox regression models with propensity score weighting for inverse probability of treatment were used. Results Of 25 357 patients, 24.8% underwent LND (2001–2007: 3 167 patients vs 2008–2013: 3 133 patients). The median NRN was 3 (interquartile range [IQR]: 1‐7). Positive nodes were identified in 17.1%: 9.3% of pT 2 and 21.6% of pT 3 patients, who underwent LND . The median NPN was 2 (IQR: 1‐3). In multivariable models, LND did not decrease CSM (hazard ratio [ HR ] 1.29; P < 0.001). LND extent, defined as NRN , did not decrease CSM ( HR 0.94; P = 0.3). Finally, multivariable models testing the effect of NPN showed increased CSM in pT 3 but not in pT 2 patients ( HR 1.29 and 1.58, P = 0.02 and P = 0.1, respectively). NRN exerted a protective effect on CSM in patients with positive nodes ( HR 0.98; P = 0.007). Conclusion In contemporary and historical patients LND or its extent do not protect from CSM . However, the NPN increases the rate of CSM in pT 3 patients. Consequently, LND and its extent appear to have little if any therapeutic value in pT 2–3 N any M 0 patients, besides its prognostic impact. High‐risk non‐metastatic patients may represent a target population for a multi‐institutional prospective 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".