2025 SCAI Clinical Practice Guidelines for the Management of Chronic Venous Disease
Bibliographic record
Abstract
Background: Chronic venous disease (CVD) is a common vascular condition that can have debilitating effects on quality of life and daily function. The Society for Cardiovascular Angiography & Interventions (SCAI) sought to develop evidence-based guidelines to support patients, clinicians, and other stakeholders in their treatment decisions about management of CVD. Methods: SCAI convened a balanced multidisciplinary guideline panel to minimize potential bias from conflicts of interest. The Evidence Foundation, a registered 501(c)(3) nonprofit organization, provided methodological support for the development of the guidelines. The guideline panel formulated and prioritized clinical questions following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach in population, intervention, comparison, outcome format. A technical review team of clinical and methodological experts conducted systematic reviews of the published evidence, synthesized data, and graded the certainty of the evidence across outcomes. The guideline panel then reconvened to develop recommendations and supporting remarks informed by the results of the technical review, as well as additional contextual factors described in the GRADE evidence-to-decision framework. Results: The guideline panel reached consensus on 9 recommendations to address variations in treatment of CVD across 8 different clinical scenarios. The panel also identified 4 anatomical scenarios with significant knowledge gaps. Conclusions: Key recommendations address patient selection for compression therapy, ablation of saphenous and perforator veins, sclerotherapy, phlebectomy, and deep vein revascularization. Two algorithms for the management of symptomatic varicose veins and venous ulcer disease were created to facilitate implementation of these evidence-based recommendations. The panel also identified several anatomical and clinical areas where future research is needed to advance the CVD field.
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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 | 0.024 | 0.102 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.033 | 0.029 |
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".