Increased risk of preoperative venous thromboembolism in patients with renal cell carcinoma and tumor thrombus
Bibliographic record
Abstract
BACKGROUND: The clinical impact of a tumor thrombus in renal cell carcinoma (RCC) patients awaiting radical nephrectomy and thrombectomy is unknown. OBJECTIVE: To determine the incidence of venous thromboembolism (VTE) in RCC patients with tumor thrombus prior to nephrectomy. PATIENTS AND METHODS: We conducted a retrospective cohort study including all late-stage (stage 3-4 excluding T1-2 N0M0) RCC patients who underwent radical nephrectomy at our institution between 1 January 2005 and 1 July 2012. Tumor thrombus was defined as the presence of an intraluminal filling defect in the renal vein, hepatic vein, portal vein, or inferior vena cava, directly extending from a renal mass detected on computed tomography. RESULTS: A total of 176 patients were included in the study. Fifty-three (30.1%) patients had tumor thrombus diagnosed on imaging Three patients with tumor thrombus (5.7%; 95% confidence interval [CI] 1.4-16.8) developed a VTE while awaiting radical nephrectomy, whereas none (0%; 95% CI 0-2.9) of the patients without a tumor thrombus had an event (P = 0.026). All three events were deep vein thrombosis. Times from tumor thrombus diagnosis to VTE were 5, 15 and 21 days. CONCLUSIONS: Tumor thrombus on imaging is a frequent finding among RCC patients awaiting nephrectomy. The presence of tumor thrombus in these patients increases the incidence of preoperative VTE.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".