Metastatic Renal Cell Cancer after Laparoscopic Radical Nephrectomy: Long-Term Follow-Up
Bibliographic record
Abstract
OBJECTIVE: To assess the risk of metastatic disease in longer-term follow-up of patients undergoing laparoscopic radical nephrectomy with morcellation for renal cell carcinoma (RCC). PATIENTS AND METHODS: We present the findings at follow-up at 13.5 to 70 months (mean 33.4 months) of 57 previously reported patients. Three, all of whom initially had clinical stage N0M0 disease, were found to have metastases. One, who had a clinical stage T3 grade III/IV tumor, developed an asymptomatic recurrence in the renal fossa with associated chest metastasis 14 months postoperatively. The second, who had a clinical stage T2 grade II/IV tumor, developed painful bony lesions and a chest metastasis 20 months postoperatively. The third patient, with a clinical stage T3 grade IV/IV tumor, was found to have a solitary port-side abdominal-wall recurrence with no other evidence of metastatic disease at 25 months. CONCLUSIONS: Longer-term follow-up has demonstrated a 5% (3/57) rate of metastases after laparoscopic radical nephrectomy. In two of these patients, the course was consistent with the natural history of RCC; however, the third had a port-site recurrence. Thus, it behooves us to be meticulous with our technique and to follow patients closely after laparoscopic nephrectomy. Several suggestions are made to reduce the likelihood of port-site recurrence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".