Stage‐specific impact of pelvic lymph node dissection on survival in patients with non‐metastatic bladder cancer treated with radical cystectomy
Bibliographic record
Abstract
Study Type – Therapy (cohort) Level of Evidence 2b What's known on the subject? and What does the study add? In patients treated with radical cystectomy, pelvic lymph node dissection may have a beneficial effect on cancer control outcomes. We examined the effect of pelvic lymph node dissection on stage‐specific cancer control outcomes. OBJECTIVE To examine the effect of stage‐specific pelvic lymph node dissection (PLND) on cancer‐specific (CSM) and overall mortality (OM) rates at radical cystectomy (RC) for bladder cancer. METHODS Overall, 11 183 patients were treated with RC within the Surveillance, Epidemiology, and End Results database. Univariable and multivariable Cox regression analyses tested the effect of PLND on CSM and OM rates, after stratifying according to pathological tumour stage. RESULTS Overall, PLND was omitted in 25% of patients, and in 50, 35, 27, 16 and 23% of patients with respectively pTa/is, pT1, pT2, pT3 and pT4 disease ( P < 0.001). For the same stages, the 10‐year CSM‐free rates for patients undergoing PLND compared with those with no PLND were, respectively, 80 vs 71.9% ( P = 0.02), 81.7 vs 70.0% ( P < 0.001), 71.5 vs 56.1% ( P = 0.001), 43.7 vs 38.8% ( P = 0.006), and 35.1 vs 32.0% ( P = 0.1). In multivariable analyses, PLND omission was associated with a higher CSM in patients with pTa/is, pT1 and pT2 disease (all P ≤ 0.01), but failed to achieve independent predictor status in patients with pT3 and pT4 disease (both P ≥ 0.05). Omitting PLND predisposed to a higher OM across all tumour stages (all P ≤ 0.03). CONCLUSIONS Our results indicate that PLND was more frequently omitted in patients with organ‐confined disease. The beneficial effect of PLND on cancer control outcomes was more evident in these patients than in those with pT3 or pT4 disease. PLND at RC should always be considered, regardless of tumour stage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".