Large Retroperitoneal Lymphadenopathy As a Predictor of Venous Thromboembolism in Patients With Disseminated Germ Cell Tumors Treated With Chemotherapy
Bibliographic record
Abstract
PURPOSE: Cisplatin-based chemotherapy, a mainstay of treatment for disseminated germ cell tumors (GCTs), is associated with venous thromboembolism (VTE). Many patients with disseminated GCTs have large retroperitoneal lymph node (RPLN) metastases that may cause venous stasis and increase the risk of VTE development. We hypothesized that there was an association between large RPLN and chemotherapy-associated VTE risk. PATIENTS AND METHODS: The training cohort was composed of patients with disseminated GCT receiving first-line chemotherapy at Princess Margaret Cancer Centre between January 2000 and December 2010. Large RPLN was defined as more than 5 cm in maximal axial diameter. The predictive and discriminatory accuracies of a model using large RPLN in predicting VTE were compared with high-risk Khorana score (≥ 3) using logistic regression and area under receiver operator characteristic curves (AUROCs). The model was externally validated in a cohort of patients treated at the London Health Sciences Centre. RESULTS: The training cohort comprised 216 patients, 21 (10%) of whom developed VTE during chemotherapy. VTE was associated with large RPLN (odds ratio [OR], 5.26; P = .001), high-risk Khorana score (OR, 11.8; P < .001), intermediate-/poor-risk disease (OR, 3.76; P = .005), and hospitalization during chemotherapy (OR, 4.24; P = .002). Large RPLN showed higher discriminatory accuracy than high-risk Khorana score (AUROC, 0.71 v 0.67, respectively). Superior discriminatory accuracy of large RPLN over high-risk Khorana score was validated in the London cohort (AUROC, 0.61 v 0.57, respectively). CONCLUSION: Large RPLN is associated with VTE in patients with disseminated GCT and provides higher discriminatory accuracy than high-risk Khorana score. Results should be validated in larger, prospective studies. Prophylactic anticoagulation may be considered in high-risk patients.
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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 | 0.005 |
| 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 | 0.001 | 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".