Total Iliocaval Reconstruction in a Complex Palliative Patient with Malignant Inferior Vena Cava Syndrome
Bibliographic record
Abstract
Inferior vena cava (IVC) compression secondary to mass effect is accompanied by edema, ascites, back and abdominal pain, and central nervous system symptoms. Most IVC syndrome cases described in the literature focus on the focal treatment of IVC lesions, and reports of complete iliocaval reconstructions secondary to malignant IVC syndrome in the palliative context are limited. In this case report, we describe the clinical presentation, technical approach, and symptomatic outcomes of a patient with extensive malignant compression and invasion of the iliofemoral venous system. An 82-year-old male with metastatic lung cancer invading the right upper quadrant of the abdomen presented with scrotal and bilateral lower extremity edema, as well as anasarca. Computed tomography (CT) demonstrated an 11 cm right adrenal metastasis and extensive retroperitoneal lymphadenopathy resulting in the compression of the IVC and iliac veins. Femoral venography demonstrated extensive collateral venous pathway formation with the opacification of the para-lumbar and vertebral veins, in addition to the vertebral/sacral venous plexus. Iliocaval reconstruction was performed using venous-dedicated stents. This case report highlights a technically successful total iliocaval reconstruction in a complex palliative patient with diffuse metastatic disease resulting in IVC compression and syndrome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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".