Suboptimal use of pelvic lymph node dissection: Differences in guideline adherence between robot-assisted and open radical prostatectomy
Bibliographic record
Abstract
INTRODUCTION: Our aim was to assess adherence to National Comprehensive Cancer Network (NCCN) and American Urological Association (AUA) guidelines for pelvic lymph node dissection (PLND) at the time of either robot-assisted (RARP) or open radical prostatectomy (ORP). METHODS: We relied on the Surveillance, Epidemiology, and End Results-Medicare linked database and focused on localized prostate cancer (PCa) patients who were treated with either RARP or ORP between October 2008 and December 2009. Categorical and multivariable logistic regression analyses targeted two endpoints: 1) probability of guideline-recommended PLND; and 2) probability of no PLND, when not guideline-recommended. RESULTS: Among 5268 PCa patients, adherence to NCCN PLND guideline was 56.9% during RARP and 76.5% during ORP (odds ratio [OR] 0.4, 95% confidence interval [CI] 0.3‒0.6). AUA PLND guideline adherence was 68.1% during RARP and 82.4% during ORP (OR 0.7, 95% CI 0.5‒0.9). When PLND was not recommended, it was more frequently performed during ORP according to either NCCN (OR 3.7, 95% CI 3.5‒3.9) or AUA (OR 2.7, 95% CI 2.6‒2.8). According to the NCCN guideline, at recommended PLND in ORP patients, 6.3% harboured lymph node invasion (LNI) (number needed to treat [NNT] 16) vs. 3.2% at RARP (NNT 31). According to the AUA guideline, at recommended PLND in ORP patients, 12.3% harboured LNI (NNT 8) vs. 5.1% RARP (NNT 19). CONCLUSIONS: Adherence to NCCN and AUA PLND guidelines was lower during RARP than during ORP when PLND was recommended. The rate of non-recommended PLND was also higher during ORP than during RARP. Technical considerations may be at play.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".