Primary retroperitoneal lymph node dissection in clinical stage 2a/b non‐seminomatous germ cell tumour
Bibliographic record
Abstract
OBJECTIVES: To reassess the role of primary retroperitoneal lymph node dissection (RPLND) in patients with marker-negative non-seminomatous germ cell tumour (NSGCT) clinical stage (CS) 2a, to explore results in patients with CS 2b and to evaluate surgical methods, recurrence, and adjuvant chemotherapy indications. MATERIALS AND METHODS: Data from 17 institutions were collected, comprising 305 men who underwent primary RPLND for CS 2 NSGCT. Regression analyses were conducted to predict histology in the RPLND specimen and disease-free survival (DFS). RESULTS: A larger retroperitoneal lymph node diameter was associated with pure teratoma in the RPLND specimen (odds ratio [OR] 1.02, 95% confidence interval [CI] 1.01-1.07; P = 0.03), but no association was observed with DFS. The 5-year DFS rates in marker negative CS 2a and 2b were 79% and 76%. In men with non-teratomatous viable cancer in the RPLND specimen, the 5-year DFS rates for CS 2a and 2b were 95% and 87% with adjuvant chemotherapy, and 67% and 74% without adjuvant chemotherapy. We did not identify an association between the number of adjuvant chemotherapy cycles and DFS. CONCLUSIONS: Our study suggests considering primary RPLND not only in marker-negative CS 2a but also in CS 2b. Further research should determine the efficacy of primary RPLND in men with CS 2c and marker-positive CS 2, as well as which patients may benefit from adjuvant chemotherapy and the optimal cycle number.
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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 | 0.002 |
| 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) | 0.001 | 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".