The prognostic value of <scp>lymph node</scp> staging with prostate‐specific membrane antigen ( <scp>PSMA)</scp> positron emission tomography/computed tomography (PET/ <scp>CT)</scp> and extended pelvic lymph node dissection in <scp>node‐positive</scp> patients with prostate cancer
Bibliographic record
Abstract
OBJECTIVES: To investigate whether patients with suspected pelvic lymph node metastases (molecular imaging [mi] N1) on staging prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) had a different oncological outcome compared to those in whom the PSMA PET/CT did not reveal any pelvic lymph node metastases (miN0). PATIENTS AND METHODS: All patients with pelvic lymph node metastatic (pN1) disease after robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND) between January 2017 and December 2020 were included. To assess predictors of biochemical progression of disease after RARP, a multivariable Cox regression analysis was performed, including number of tumour-positive lymph nodes, diameter of the largest nodal metastasis, and extranodal extension. RESULTS: In total, 145 patients were diagnosed with pN1 disease after ePLND. The median biochemical progression-free survival in patients with miN0 on PSMA PET/CT was 13.7 months, compared to 7.9 months in patients with miN1 disease (P = 0.006). On multivariable Cox regression analysis, both number of tumour-positive lymph nodes (>2 vs 1-2: hazard ratio [HR] 1.97; P = 0.005) and diameter of the largest nodal metastasis (HR 1.12; P < 0.001) were significant independent predictors of biochemical progression of disease. CONCLUSION: Patients in whom pelvic lymph node metastases were suspected on preoperative PSMA imaging (miN1), patients diagnosed with >2 tumour-positive lymph nodes, and patients with a larger diameter of the largest nodal metastasis had a significantly increased risk of biochemical disease progression after surgery.
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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 | 0.002 |
| 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 | 0.001 | 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".