Clinical and Radiological Correlation of Low Flow Vascular Malformation Treated With Percutaneous Sclerotherapy
Bibliographic record
Abstract
Objective: To retrospectively correlate imaging findings post-sclerotherapy of low-flow vascular malformations with clinical outcome. Materials and Methods: We retrospectively evaluated 81 pediatric patients who had sclerotherapy in our department over a 14-year period. Patients with a diagnosis of low-flow vascular malformation, pre and post-treatment ultrasound (US) and clinical follow-up evaluation were included in the study. Exclusion criteria were coexisting high-flow vascular malformations, history of additional surgical or medical treatment to their malformation and large infiltrative lesions difficult to measure on US. Pre and post-treatment sonographic volumes of the malformation were assessed. Changes in volume were categorized into 6- increased volume, stable and volume decrease of 1-25%/26-50%/51-75%/75-100%. Clinical outcomes were categorized into 4 – worse, no change, improved and symptom free. In cases where pre-treatment MRI was available, the estimated malformation volumes in both modalities were correlated using Spearman’s rank correlation. The change in sonographic volume was correlated with clinical outcome using Spearman’s rank correlation. P-values < .05 were considered significant. Results: Twenty-nine patients were included in the study; 13 with venous malformation (VM), and 16 with lymphatic malformation (LM). Nineteen patients had both pre-treatment US and MRI, showing correlation in volume between the 2 modalities ( P < .001). Post-treatment change in volume correlated with clinical outcome for combined venous and LMs (rho = .44, P = .02). No correlation was found when venous (rho = .48, P = .09) and lymphatic (rho = .33, P = .21) malformations were considered separately. Conclusion: Ultrasound can potentially be used as an objective tool in evaluating sclerotherapy treatment response of low-flow vascular malformations in the pediatric population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".