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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it