Superior Vena Cava Syndrome: Role of the Interventionalist
Bibliographic record
Abstract
Superior vena cava syndrome results from the obstruction of blood flow through the superior vena cava and is most often due to thoracic malignancy. However, benign etiologies are on the rise secondary to more frequent use of intravascular devices such as central venous catheters and pacemakers. Although rarely a medical emergency, the symptoms can be alarming, particularly to the patient. Traditionally, superior vena cava syndrome has been managed with radiotherapy and chemotherapy. But interventional endovascular techniques have made inroads that offer a safe, rapid, and durable response. In many cases, it may be the only reasonable treatment. Because of this, an approach to endovascular treatment of this condition must be in the armamentarium of the interventional radiologist. This review will provide the reader with an insight into the etiology, pathophysiology, and various management principles of superior vena cava syndrome. The focus will be on understanding the techniques used during various endovascular interventions, including angioplasty, stenting, and pharmacomechanical thrombolysis. Discussion will also be centred on possible complications and current evidence as well as controversies regarding these approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".