Lymph node count threshold for optimal pelvic lymph node staging in prostate cancer
Bibliographic record
Abstract
OBJECTIVES: To test the relationship between the extent of pelvic lymph node dissection at radical prostatectomy and the rate of lymph node metastases, and to identify the ideal number of lymph nodes that should be removed to achieve an optimal staging. METHODS: We assessed 20 789 prostate cancer patients treated with radical prostatectomy and pelvic lymph node dissection between 2004 and 2006. Receiver operating characteristics analyses were used to define the probability of correctly staging lymph node metastases patients according to lymph node count. Univariable and multivariable regression analyses tested the relationship between lymph node count and lymph node metastases rate. RESULTS: The average lymph node count was 6.4 (median 5.0). Overall, the lymph node metastases rate was 2.5%; and it resulted to be 0.2, 1.5 and 6.7% in low, intermediate and high-risk tumors, respectively. The rate of lymph node metastases was 3.5 and 6.7% in patients with 10 and 20 lymph node count, respectively. Removing 20 lymph nodes yielded a 90% probability of correctly staging lymph node metastases, regardless of risk group. In multivariable analysis, lymph node count was an independent predictor of lymph node metastases stage (odds ratio: 1.07, P < 0.001). CONCLUSIONS: A direct relationship might exist between the extent of pelvic lymph node dissection and the lymph node metastases rate. An extended pelvic lymph node dissection with at least 20 lymph nodes would offer correct lymph node staging in 90% of cases, regardless of tumor characteristics. This cut-off might be considered adequate by most surgeons. Such a high lymph node yield necessitates an anatomically extended pelvic lymph node dissection.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".