Large retroperitoneal lymphadenopathy and increased risk of venous thromboembolism in patients receiving first‐line chemotherapy for metastatic germ cell tumors: A study by the global germ cell cancer group (G3)
Bibliographic record
Abstract
BACKGROUND: Metastatic germ cell tumor (mGCT) patients receiving chemotherapy have increased risk of life-threatening venous thromboembolism (VTE). Identifying VTE risk factors may guide thromboprophylaxis in this highly curable population. METHODS: Data were collected from mGCT patients receiving first-line platinum-based chemotherapy at 22 centers. Predefined variables included International Germ Cell Cancer Collaborative Group (IGCCCG) risk classification, long-axis diameter of largest retroperitoneal lymph node (RPLN), Khorana score, and use of indwelling vascular access device (VAD). VTE occurring at baseline, during chemotherapy and within 90 days, was analyzed. RESULTS: Data from 1135 patients were collected. Median age was 31 years (range 10-74). IGCCCG risk was 64% good, 20% intermediate, and 16% poor. VTE occurred in 150 (13%) patients. RPLN >3.5 cm demonstrated highest discriminatory accuracy for VTE (AUC 0.632, P < .001) and was associated with significantly higher risk of VTE in univariable analysis (22% vs 8%, OR 3.0, P < .001) and multivariable analysis (OR 1.8, P = .02). Other significant risk factors included, Khorana score ≥3 (OR 2.6, P = .008) and VAD use (OR 2.7, P < .001). CONCLUSIONS: Large RPLN and VAD use are independent risk factors for VTE in mGCT patients receiving chemotherapy. VAD use should be minimized in this population and thromboprophylaxis might be considered for large RPLN.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".