One-Step Approach to Treating Venous Insufficiency
Bibliographic record
Abstract
BACKGROUND: Patients with venous insufficiency can be treated with office-based, minimally invasive means like radiofrequency ablation (RFA) and ultrasound-guided foam sclerotherapy (UGFS). Traditional treatment involves ablation of the great saphenous vein (GSV) and the short saphenous vein (SSV) with RFA as a first step. The remaining refluxing tributaries are treated at a later session with UGFS or microphlebectomy. This approach is associated with an increased risk of thrombophlebitis while awaiting the second procedure. We, instead, elected to treat all the refluxing veins in one procedure which combines RFA of the truncal and perforating vein with UGFS to the accessory and tributary veins. METHODS: A controlled non-randomized clinical trial, in which a total of 72 extremities were treated for vein incompetence in 63 consecutive patients aged 26 - 78 years, was conducted. Sixty-three extremities (87.5%) received treatment for reflux in GSV, 10 extremities (13.9%) were treated for reflux in SSV, and 11 (15.3%) were treated for reflux in the perforators. Reflux duration > 1 second to increase specificity and truncal vein diameter > 5 mm were identified in the treated limbs. The treatment was performed at our office and it involved delivering radiofrequency thermal energy to the truncal and perforating vein and then using foam sclerotherapy with the guidance of ultrasound to close the tributary and accessory veins in a single procedure. The results were monitored at 1 week and 6 weeks post-operatively by venous duplex ultrasound. RESULTS: One hundred percent of the treated GSV and SSV and 91.7% of tributary veins were completely closed after the index procedure. Only 10 of 72 extremities (13.9%) needed a follow-up treatment to achieve closure of the perforator and accessory veins. By combining RFA with UGFS, our cohort did not experience thrombophlebitis or deep vein thrombosis (DVT) post-operatively. No major or minor complications were found upon follow-up evaluation. CONCLUSION: We believe that combining RFA with UGFS in a solo practice lowers the incidence of thrombophlebitis in the tributaries. Using this approach allowed us to achieve more complete resolution of venous reflux disease with lower complication rates in comparison with the popular staged strategy. This could have implications for financial savings to both the patient and the health system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".