Effectiveness of Surgical Interventions in Chronic Venous Insufficiency: An Evidence-Based Systematic Review
Bibliographic record
Abstract
Chronic Venous Insufficiency (CVI) is a disease that can significantly affect patients' quality of life. Although modern surgical therapies such as endovenous thermal ablation (ETA), iliac vein stenting, and others have been introduced, the effectiveness and safety of each procedure needs to be further evaluated. This study aims to evaluate the effectiveness and safety of modern surgical interventions in the management of CVI, focusing on clinical outcomes such as ulcer healing, pain reduction, and improvement in patient quality of life. This systematic review was conducted following PRISMA guidelines by screening literature from PubMed, ScienceDirect, Google Scholar, and Cochrane Library between 2014 and 2024. Of the 17,500 articles identified, 10 studies were included through a rigorous selection process. Analysis was performed based on study design, population, intervention type, clinical outcomes, and safety profile. Endovenous thermal ablation showed an anatomical success rate of more than 96% with long-term clinical benefits. Iliac vein stenting significantly improved symptoms of deep vein obstruction (VAS: 9 to 2.5) with a primary patency rate of 92%. Methods such as mechanochemical ablation (MOCA) and cyanoacrylate embolization (CAE) offer minimally invasive approaches with low complication rates and fast recovery times. The combination of early ablation and compression therapy accelerates healing of chronic venous ulcers (median healing time: 56 vs. 82 days). Modern surgical interventions are proven effective and safe in the management of CVI, with thermal ablation as the gold standard and iliac vein stenting as the superior option for deep vein obstruction. This evidence-based approach provides important guidance for personalizing CVI therapy in the future. Further research is needed to evaluate long-term sustainability and to optimize evidence-based clinical guidelines.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".