Management strategies and clinical outcomes in patients with inferior vena cava thrombosis: Data from GARFIELD‐VTE
Bibliographic record
Abstract
BACKGROUND: Inferior vena cava (IVC) thrombosis is a rare form of venous thromboembolism (VTE). The optimal treatment strategies and outcomes are unclear in patients with this presentation. OBJECTIVE: We aimed to compare baseline characteristics, treatment patterns and 24-month outcomes in IVC thrombosis patients (n = 100) with lower extremity deep vein thrombosis (LEDVT) patients (n = 7629). METHODS: GARFIELD-VTE is a prospective, observational registry of 10 868 patients with objectively diagnosed VTE from 415 sites in 28 countries. RESULTS: IVC thrombosis patients were younger (51.9 vs. 59.8 years), more frequently had active cancer (26.0% vs. 8.9%) or history of cancer (21.0% vs. 12.2%), and less frequently had recent trauma or surgery than LEDVT patients. IVC thrombosis was more frequently treated with parenteral anticoagulants alone (35.1% vs. 15.9%), whereas patients with LEDVT more commonly received vitamin K antagonists (32.0% vs. 25.8%) or direct oral anticoagulants (49.0% vs. 35.1%). Thrombolysis (11.0% vs. 3.6%) and surgical/mechanical interventions (4.0% vs. 1.4%) were more frequent in IVC thrombosis. At 24-months, the rate per 100 person-years (95% confidence interval) of all-cause mortality was higher in patients with IVC thrombosis than LEDVT (13.28 [8.57-20.58] vs. 4.91 [4.55-5.3]); the incidence of cancer-associated mortality was comparable as was the incidence of VTE recurrence (4.11 [1.85-9.15] vs. 4.18 [3.84-4.55]). Major bleeding was slightly higher in IVC thrombosis (2.03 [0.66-6.31] vs. 1.66 [1.45-1.89]). CONCLUSION: In summary, IVC thrombosis patients have higher all-cause mortality rates than those with LEDVT, a finding only partly attributable to malignancy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".