Large retroperitoneal lymph nodes (RPLN) as a novel risk factor for venous thromboembolism (VTE) in germ cell tumor (GCT) patients (pts) receiving first-line chemotherapy (chemo).
Bibliographic record
Abstract
4535 Background: VTE causes substantial morbidity and mortality in GCT pts. The Khorana model is a validated predictive model that stratifies VTE risk in cancer pts receiving chemo; Khorana score ≥3 (K3) identifies pts at high risk. Many GCT pts have bulky RPLN that can cause venous stasis in the lower limbs. This study examines the incidence of VTE in GCT pts and assesses large RPLN as a novel predictor of VTE risk. Methods: Retrospective data from the Princess Margaret Hospital GCT database was complemented by review of medical records. GCT pts receiving 1st line chemo between 2000-2010 were included. Pts diagnosed with VTE prior to chemo and pts receiving thromboprophylaxis (TP) were excluded. Pts diagnosed with VTE during or within 3 months of completing chemo were identified. Large RPLN were defined as having maximal diameter ≥5cm. Odds ratios for VTE risk with large RPLN and K3 were calculated. Discriminatory accuracy (DA) of each predictor was calculated using area under the receiver operating characteristic curves (AUROC). An external cohort from London Regional Cancer Program, with similarly collected data, was used to validate results. Results: In the test cohort 21 (10%) of 216 pts developed VTE. Both large RPLN (OR 6.2, p<0.001) and K3 (OR 11.8, p<0.001) were significantly associated with VTE. Positive predictive value (PPV) was lower for large RPLN compared to K3 (21% v 44%, p<0.014) but sensitivity was greater (71% v 40%, p=0.001), while DA showed no difference (AUROC 0.73 v 0.67, p=0.46). In the validation cohort 10 (9%) of 111 pts developed VTE. There was a non significant trend for associations between VTE and both large RPLN (OR 2.54, p=0.16) and K3 (OR 4.5, p=0.13). When compared to K3, sensitivity was greater for large RPLN (60% v 20%, p=0.003), but there was no difference in PPV (14% v 29%, p=0.25) or DA (AUROC 0.61 v 0.57, p=0.72). Conclusions: VTE occurs in 1 in 10 GCT pts receiving curative chemotherapy. Large RPLN is a novel and clinically applicable predictor of VTE risk, although prospective validation would be beneficial. Randomized controlled trials of TP in GCT pts should be considered in pts at high risk of VTE, identified by large RPLN or K3.
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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 | 0.002 |
| 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".