Why all prostate cancer surgery should include an adequate lymph node dissection
Bibliographic record
Abstract
The role of a lymph node dissection in most cancer sites portends a benefit from accurate staging and assignment of adjuvant therapy or, possibly, a direct therapeutic effect by local/regional control. The adequacy of this regional dissection has become an important quality of care indicator (i.e., colon, rectal, testes and bladder). The adoption of recommendations to limit lymphadenectomy in other sites has generally followed prospective studies (i.e., uterine) or established predictive tools (i.e., sentinel biopsy in melanoma, breast cancer). This issue is controversial for prostate cancer management given the lack of prospective data and ambiguous retrospective studies1 and is illustrated in the variation in our clinical practice guidelines (Table 1).2–5 Table 1. Overview of clinical practice guidelines on prostate cancer management Coincident with the decrease of lymph node involvement (LNI) in most prostatectomy series6,7 there has been remarkable decline in pelvic lymph node dissection (PLND) for low-risk disease,8 although this trend may be less apparent in Canada.9 A risk-adapted approach to PLND remains controversial; it has been suggested that other complicating elements are involved in its decline, including changes in surgical approach as well as reimbursement issues.10 But what is the evidence to abandon this concept of regional control for prostate cancer in patients with perceived low-risk disease? Without prospective randomized data the argument to omit PLND generally revolves around the following three issues: staging, therapeutic benefit and side effects.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".