Large retroperitoneal lymph nodes (RPLN) as a predictor for venous thromboembolism (VTE) in patients (pts) with germ cell tumor (GCT) receiving first-line chemotherapy (chemo).
Bibliographic record
Abstract
332 Background: VTE causes significant morbidity and mortality in GCT pts. While an existing and validated predictive model identifies VTE risk in chemo pts with any cancer (Khorana model), a predictive model specific to GCT does not exist. Many GCT pts present with bulky RPLN that produce venous stasis in the lower extremities. The objective of this study was to explore the association between large RPLN and VTE in GCT pts receiving chemo and compare large RPLN as a predictor for VTE in GCT pts to the non-GCT specific Khorana model. Methods: Clinical data from our institutional GCT database was complemented by review of radiology, pharmacy and medical records. All GCT pts receiving 1st line chemo between 1-Jan-00 and 31-Dec-10 were included. Large RPLN were defined as ≥5cm in maximal diameter. Factors used in the Khorana model (baseline BMI, hemoglobin, white cell count and platelets) were collected. We compared the predictive accuracy of large RPLN versus Khorana score ≥3 using receiver operator characteristic (ROC) curve statistical analyses. Results: The cohort consisted of 260 GCT pts, median age 31.5 years, predominantly testis primary (235, 90%) and good risk (171, 66%). 17 (7%) developed VTE prior to the start of chemo. 19 (7%) were given prophylactic anticoagulation, none of whom developed VTE. Of the remaining 224 pts, 20 (9%) developed VTE during chemo. In a univariate analysis, large RPLN was strongly associated with VTE (OR 7.74, p<0.001), as were Khorana score ≥3 (OR 9.81, p<0.001) and hospital admission during chemo (OR 3.96, p=0.004). ROC curve analyses demonstrated large RPLN was a significant individual predictor for VTE (AUC 0.588, p=0.03), however, Khorana score ≥3 was a better predictor (AUC 0.664, p=0.02). Adding large RPLN to create a modified Khorana score provided marginal gains (AUC 0.682, p=0.02). Conclusions: Although large RPLN at diagnosis predicts for VTE in GCT pts, the Khorana predictive model is superior. Given the high rate of VTE in GCT pts receiving chemo, we recommend prophylactic anticoagulation for pts at increased risk, including pts with Khorana score ≥3, pts requiring hospital admission or pts with large RPLN.
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 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 | 0.002 |
| 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 | 0.000 | 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".