Assessing the minimum number of lymph nodes needed at radical cystectomy in patients with bladder cancer
Bibliographic record
Abstract
OBJECTIVE: To identify the likelihood of finding one or more positive lymph nodes (LNs) according to the number of LNs removed at radical cystectomy (RC), as the number of LNs removed affects disease progression and survival after RC. PATIENTS AND METHODS: Between 1984 and 2003, 731 assessable patients had RC and bilateral pelvic lymphadenectomy at three different institutions. ROC curve coordinates were used to determine the probability of identifying one or more positive LNs according to the total number of removed LNs. RESULTS: Of the 731 patients, 174 (23.8%) had LNs metastases. The mean (median, range) number of LNs removed was 18.7 (17, 1-80). The ROC coordinate-based plots of the number of removed LNs and the probability of finding one or more LNs metastases indicated that removing 45 LNs yielded a 90% probability. Conversely, removing either 15 or 25 LNs indicated, respectively, 50% and 75% probability of detecting one or more LNs metastases. CONCLUSIONS: These data indicate that removing 25 LNs might represent the lowest threshold for the extent of lymphadenectomy at RC. Our findings confirm the importance of an extended lymph node dissection.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".