Decision Analysis of Pelvic Lymph Node Dissection During Radical Prostatectomy
Bibliographic record
Abstract
PURPOSE: There is controversy about the decision of whether to perform a pelvic lymph node dissection (PLND) during radical prostatectomy for prostate cancer. While a recent randomized trial reported a reduced risk of metastasis for extended compared with limited PLND, some guidelines do not recommend PLND, at least partly on the basis that it raises the risk of complications such as lymphocele. We conducted a decision analysis of PLND. Our aim was to put varying numerical estimates on benefit, harm, and uncertainty to determine whether, and under what conditions, PLND would do more good than harm. MATERIALS AND METHODS: Our approach was to start first with a simple decision tree for PLND vs no PLND during radical prostatectomy and then determine whether added complexity would be of benefit. We started by using inputs that were unfavorable to PLND-for instance, using an extremely high outlying rate of lymphocele and having no difference in metastasis rates beyond 10 years-aiming to vary these in sensitivity analyses. RESULTS: Despite starting with unfavorable inputs for PLND, the expected utility of PLND was higher than that for no PLND across a broad range of scenarios, including giving a low subjective probability that PLND was of benefit, high risk of PLND complications, and PLND's reduction in locoregional metastases being considered irrelevant. PLND was also favored in patients with prostate-specific membrane antigen positron emission tomography-negative disease, a finding driven by the imperfect sensitivity of prostate-specific membrane antigen positron emission tomography. The key limitation is that the findings do not apply to patients who have only a trivial risk of metastasis, such as patients with grade group 1 disease. CONCLUSIONS: PLND should be the standard of care for patients undergoing radical prostatectomy for grade group 2 or higher disease.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".