A standardized protocol for identifying and counting lymph nodes harvested by pelvic lymph node dissection at the time of radical cystectomy
Bibliographic record
Abstract
INTRODUCTION: Lymph node counts have become a surrogate measure for the extent and quality of pelvic lymph node dissection (PLND) at radical cystectomy, but little consideration has been given to the methodology of lymph node processing. We report results from a prospective series comparing a conventional protocol for processing PLND specimens to a fat-emulsifying protocol. We hypothesized that the rate of node positivity would increase with the fat-emulsifying protocol. METHODS: Patients undergoing radical cystectomy for cTis-T4aN0-1M0 urothelial carcinoma of the bladder were eligible for this trial. Palpable lymph nodes were isolated from the PLND specimens in the conventional protocol. The remaining tissue was then processed with fat-emulsifying solution to identify further nodes visually. Nodal counts were compared between techniques. RESULTS: The median number of nodes counted in the PLND specimens of 26 patients was 24.5 (range: 20-40) with conventional processing and 37 (range: 24-52) with the fat-emulsifying solution (p < 0.001). Three patients had lymph node positive disease detected by conventional means, and a single patient was found to have a single positive node by the fat-emulsifying solution alone. The study was closed early after conducting a futility analysis. CONCLUSIONS: A fat-emulsifying protocol identified more lymph nodes than a conventional protocol and may be an appropriate method to standardize lymph node processing following PLND. However, we were unable to show that such a standardized approach significantly increased the rate of node positivity in patients undergoing radical cystectomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".