Prognosis of patients with pelvic lymph node ( <scp>LN</scp> ) metastasis after radical prostatectomy: Value of extranodal extension and size of the largest <scp>LN</scp> metastasis
Bibliographic record
Abstract
OBJECTIVE: To assess the prognostic role of extranodal extension (ENE) and the size of the largest lymph node (LN) metastasis in predicting early biochemical relapse (eBCR) in patients with LN metastasis after radical prostatectomy (RP). PATIENTS AND METHODS: We evaluated BCR-free survival in men with LN metastases after RP and pelvic LN dissection performed in six high-volume centres. Multivariable Cox regression tested the role of ENE and diameter of largest LN metastasis in predicting eBCR after adjusting for clinicopathological variables. We compared the discrimination of multivariable models including ENE, the size of largest LN metastasis and the number of positive LNs. RESULTS: Overall, 484 patients were included. The median (interquartile range, IQR) follow-up was 16.1 (6-27.5) months. The median (IQR) number of removed LNs was 10 (4-14), and the median (IQR) number of positive LNs was 1 (1-2). ENE was present in 280 (58%) patients, and 211 (44%) had their largest metastasis >10 mm. Patients with ENE and/or largest metastasis of >10 mm had significantly worse eBCR-free survival (all P < 0.01). On multivariable analysis, number of positive LNs (≤2 vs >2) and the diameter of LN metastasis (≤10 vs >10 mm), but not ENE, were significant predictors of eBCR (all P < 0.003). ENE and diameter of LN metastasis increased the area under the curve of a baseline multivariable model (0.663) by 0.016 points. CONCLUSIONS: The diameter of the largest LN metastasis and the number of positive LNs are independent predictors of eBCR. Considered together, ENE and the diameter of the largest LN metastasis have less discrimination than the number of positive LNs.
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 | 0.000 |
| 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 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".