Should endovascular stenting be used routinely as first-line treatment for malignant superior vena cava syndrome?—a critical review in the context of recent advances in oncological treatments
Bibliographic record
Abstract
Malignant superior vena cava syndrome (SVCS) is no longer considered a medical emergency in most cases because it rarely leads to life-threatening complications. However, it results in disturbing symptoms that can significantly affect patients' quality of life. Treating this condition effectively while minimising treatment-related morbidity is of increasing importance as cancer patients are living longer from advances in oncological treatments. This clinical practice review discusses the implications of these advances on the decision to consider stenting as the initial treatment for SVCS. Stenting is increasingly popular as it provides quick symptomatic relief with low rates of complications. Systemic treatments have evolved in the past two decades with the development of immunotherapy and targeted therapies that have different response patterns compared to conventional chemotherapy. Furthermore, major changes have also been seen in radiotherapy techniques that allow treatments to better conform to targets while sparing normal tissues. These advances have changed practice patterns for stent placement in SVCS patients in both the localised and metastatic settings. Prospective studies using standardised patient-reported outcome tools are needed to determine the optimal treatment sequence for SVCS patients, as current recommendations are mainly based on retrospective single-arm studies. An individualized approach with multidisciplinary input is therefore important to optimize patient outcomes before more robust evidence is available.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".