Massive iliofemoral thrombosis in an 18-year-old man treated with an endovascular procedure
Bibliographic record
Abstract
Introduction: Venous thromboembolism remains a widespread global problem that occurs not only in elderly patients but also in younger individuals.Most patients receive conservative treatment for deep vein thrombosis (DVT), but 20-50% of them will develop post-thrombotic syndrome as a consequence.Material and methods: An 18-year-old man was admitted to the hospital with massive swelling and pain of the left leg.After a primary diagnostic process, computed tomography phlebography was performed, which revealed no contrast in the common iliac vein, external iliac vein, internal iliac vein, femoral vein, and great saphenous vein on the left side.The diagnosis of DVT was stated.Additionally, compression of the right common iliac artery on left iliac vein was visualized, which indicated May-Thurner syndrome.Results: Mechanical thrombectomy was performed using the AngioJet system.Additionally, local thrombolysis was performed.For better outflow from the iliac vein, stent implantation was performed using a Bentley Beyond Venous 16 × 100 mm stent.Due to a rupture of the venous stent, an additional Sinus-XL 16 × 60 mm stent and a Sinus Venous 14 × 60 mm stent were implanted.Control phlebography was performed, and procedure was ended by leaving the 6F sheath in the popliteal vein with administration of Actilyse for 24 hours.Venography performed on the next day revealed an optimal result with very good outflow.What is more, the patient reported pain relief, and the leg swelling decreased.Conclusions: Endovascular recanalization of the iliofemoral vein thrombosis is technically possible, safe, and durable.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".