Duplicated and Incompetent Accessory Great Saphenous Veins: Single-Center Outcomes of Hybrid EVLA with Minimal-Incision Ligation
Bibliographic record
Abstract
Varicose veins Chronic venous disease Endovenous laser ablationObjective Endovenous laser ablation (EVLA) is a minimally invasive treatment for varicose veins caused by incompetent saphenous veins.Thermal ablation of the great or small saphenous vein using laser or radiofrequency is the first-line therapy in the U.S., U.K., and South Korea, favored for faster recovery and fewer complications than surgical stripping.However, diode laser fibers can be rigid and deliver high energy, limiting efficacy in complex anatomies such as duplicated or accessory great saphenous veins (GSVs).These challenges call for refined approaches to improve outcomes and minimize recurrence.Methods This single-center, single-arm study included adult patients with chronic venous disease who underwent EVLA using a 1940-nm diode laser between January 2010 and December 2023.A <1.0 cm incision above the saphenofemoral junction (SFJ) enabled double high ligation of the GSV and accessory veins.Under ultrasound guidance, the laser fiber was withdrawn at 1-2 mm/s.Patients without complete follow-up data were excluded.Outcomes, complications, and procedural feasibility were assessed.Results Ten patients (9 males, 1 female) were treated.Mean age was 54.4±16.7 years; BMI, 24.0±3.9kg/m².Mean ablated lengths were 35.1±3.0 cm; mean energy delivered was 1065.1±166.4J.All veins showed complete occlusion with no recanalization.One patient (10.0%) developed endovenous heat-induced thrombosis.No other complications occurred.Conclusion This hybrid technique of minimal-incision high ligation and forward EVLA effectively treated duplicated accessory GSVs, reducing LEED, enhancing recovery, and improving outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".