The effect of lymph node dissection on cancer‐specific survival in salvage radical prostatectomy patients
Bibliographic record
Abstract
BACKGROUND: We hypothesized that lymph node dissection (LND) at salvage radical prostatectomy may be associated with lower cancer-specific mortality (CSM) and we tested this hypothesis. METHODS: We relied on surveillance, epidemiology, and end results (2004-2016) to identify all salvage radical prostatectomy patients. Categorical, as well as univariate and multivariate Cox regression models tested the effect of LND (LND performed vs. not), as well as at its extent (log-transformed lymph node count) on CSM. RESULTS: Of 427 salvage radical prostatectomy patients, 120 (28.1%) underwent LND with a median lymph node count of 6 (interquartile range [IQR], 3-11). According to LND status, no significant or clinically meaningful differences were recorded in PSA at diagnosis, stage and biopsy Gleason score at diagnosis, except for age at prostate cancer diagnosis (LND performed 63 vs. 68 years LND not performed, p < .001). LND status (performed) was an independent predictor of lower CSM (hazard ratio [HR] 0.47; p = .03). Similarly, lymph node count (log transformed) also independently predicted lower CSM (HR: 0.60; p = .01). After the 7th removed lymph node, the effect of CSM became marginal. The effect of N-stage on CSM could not be tested due to insufficient number of observations. CONCLUSIONS: Salvage radical prostatectomy is rarely performed and LND at salvage radical prostatectomy is performed in a minority of patients. However, LND at salvage radical prostatectomy is associated with lower CSM. Moreover, LND extent also exerts a protective effect on CSM. These observations should be considered in salvage radical prostatectomy candidates.
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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 | 0.004 |
| 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 | 0.000 |
| 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".