Management and outcomes in patients with tumor thrombus: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Tumor thrombus can be associated with an increased risk of venous thromboembolism (VTE) and poor prognosis. The risks and benefits of anticoagulation remain unclear. OBJECTIVES: To evaluate the role of anticoagulation and associated outcomes in patients with tumor thrombus. METHODS: We conducted a single-center retrospective cohort study in patients with tumor thrombus from 2019 to 2022. All patients were followed for 12 months from the diagnosis of tumor thrombus or until death if death occurred earlier. The primary outcome was the percentage of patients prescribed any dose of anticoagulation for tumor thrombus (or concurrent bland thrombus/VTE). The secondary outcomes included new thrombosis, major bleeding, clinically relevant nonmajor bleeding, and mortality. We calculated the 6- and 12-month cumulative incidence of outcomes with 95% CI and compared those given anticoagulation vs not, considering death as a competing risk. RESULTS: We included 211 patients, among whom 106 (50.2%; 95% CI, 47.9%-52.6%) were given anticoagulation for tumor thrombus or concurrent VTE (present in 21.8%). The most common type of cancer was hepatocellular carcinoma (28%). Splanchnic veins were the most commonly involved (49.3%). Anticoagulation was more likely used if tumor thrombus involved the inferior vena cava and/or the heart, with concurrent VTE, or if thrombosis service was consulted. The overall 12-month incidence of new VTE was 11.4% (95% CI, 7.3%-16.5%), that of major bleeding + clinically relevant nonmajor bleeding was 36.6% (95% CI, 29.6%-43.5%), and mortality of 52.5% (95% CI, 44.8%-59.6%), with no significant differences among groups given anticoagulation or not. CONCLUSION: Patients with tumor thrombus carry high risks of VTE, bleeding, and mortality. The impact of anticoagulation remains unclear.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".