Differences in rates of pelvic lymph node dissection in National Comprehensive Cancer Network favorable, unfavorable intermediate- and high-risk prostate cancer across United States SEER registries
Bibliographic record
Abstract
Background: The National Comprehensive Cancer Network (NCCN) guidelines recommend pelvic lymph node dissection (PLND) in NCCN high- and intermediate-risk prostate cancer patients. We tested for PLND nonadherence (no-PLND) rates within the Surveillance Epidemiology and End Results (2010-2015). Materials and methods: We identified all radical prostatectomy patients who fulfilled the NCCN PLND guideline criteria (n = 23,495). Nonadherence rates to PLND were tabulated and further stratified according to NCCN risk subgroups, race/ethnicity, geographic distribution, and year of diagnosis. Results: < 0.001). Over time, the no-PLND rates declined in the overall cohort and within each NCCN risk subgroup. Georgia exhibited the highest no-PLND rate (49%), whereas New Jersey exhibited the lowest (15%). Finally, no-PLND race/ethnicity differences were recorded only in the NCCN intermediate unfavorable subgroup, where Asians exhibited the lowest no-PLND rate (20%) versus African Americans (27%) versus Whites (26%) versus Hispanic-Latinos (25%). Conclusions: The lowest no-PLND rates were recorded in the NCCN high-risk patients followed by NCCN intermediate unfavorable and favorable risk in that order. Our findings suggest that unexpectedly elevated differences in no-PLND rates warrant further examination. In all the NCCN risk subgroups, the no-PLND rates decreased over time.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".