Clinical nodal staging scores for prostate cancer: a proposal for preoperative risk assessment
Bibliographic record
Abstract
BACKGROUND: Pelvic lymph node dissection in patients undergoing radical prostatectomy for clinically localised prostate cancer is not without morbidity and its therapeutical benefit is still a matter of debate. The objective of this study was to develop a model that allows preoperative determination of the minimum number of lymph nodes needed to be removed at radical prostatectomy to ensure true nodal status. METHODS: We analysed data from 4770 patients treated with radical prostatectomy and pelvic lymph node dissection between 2000 and 2011 from eight academic centres. For external validation of our model, we used data from a cohort of 3595 patients who underwent an anatomically defined extended pelvic lymph node dissection. We estimated the sensitivity of pathological nodal staging using a beta-binomial model and developed a novel clinical (preoperative) nodal staging score (cNSS), which represents the probability that a patient has lymph node metastasis as a function of the number of examined nodes. RESULTS: In the development and validation cohorts, the probability of missing a positive lymph node decreases with increase in the number of nodes examined. A 90% cNSS can be achieved in the development and validation cohorts by examining 1-6 nodes in cT1 and 6-8 nodes in cT2 tumours. With 11 nodes examined, patients in the development and validation cohorts achieved a cNSS of 90% and 80% with cT3 tumours, respectively. CONCLUSIONS: Pelvic lymph node dissection is the only reliable technique to ensure accurate nodal staging in patients treated with radical prostatectomy for clinically localised prostate cancer. The minimum number of examined lymph nodes needed for accurate nodal staging may be predictable, being strongly dependent on prostate cancer characteristics at diagnosis.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".