Image guided sclerotherapy for the treatment of venous malformations
Bibliographic record
Abstract
BACKGROUND: Percutaneous sclerotherapy is the preferred method of treatment for VMs (venous malformations). However, treatment protocols vary, and research on the most effective sclerosant is conflicting. Additionally, there is limited knowledge on the effect of sclerosant volume on treatment outcome. This study aims to determine the outcomes and complications of image-guided sclerotherapy for VMs with respect to sclerosant and sclerosant volume. Towards this, a 10-year retrospective chart review was conducted of patients with VMs treated with sclerotherapy at the Vascular Anomalies Clinic at British Columbia Children's Hospital. RESULTS: Thirty-four patients with VMs were treated of which 15 had a successful outcome, 13 had a failed outcome, and 6 had additional planned treatments after the study time period. Lesions on the head and neck or classified as type I or II had the highest success rates. Combination therapy with both ethanol and 3% STS (sodium tetradecyl sulphate), and ethanol alone had a higher success rate (64% and 60%) compared to 3% STS (11%). Major complications were most associated with 3% STS (17%) followed by ethanol (9%), and no major complications were found with combination. No relationship between the volume of sclerosant per lesion volume and outcome was found. CONCLUSIONS: Combination and ethanol were the most effective sclerosants in terms of highest success rates and lowest complication rates, and sclerosant volume per lesion volume had no effect on outcomes. Future work should aim at studying larger sample sizes to account for the multiple factors that may influence the choice of sclerosant and treatment 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".