Percutaneous Sclerotherapy for Facial Venous Malformations: Subjective Clinical and Objective MR Imaging Follow-Up Results
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Venous malformations are the most common of all vascular anomalies, 40% of which are found in the head and neck. We discuss results of percutaneous sclerotherapy using bleomycin for facial VMs by using subjective clinical assessment and objective changes on MR imaging. MATERIALS AND METHODS: Thirty-seven patients with facial VMs were treated by percutaneous sclerotherapy with bleomycin. Of these, 31 patients with 32 lesions had pre- and posttreatment MR imaging. Each lesion received between 1 and 9 sclerotherapy sessions (average, 3.5). MR findings and clinical results of treatment were retrospectively reviewed. Clinical results were based on the physician's physical examination and interview of the patient; these were classified as worse, unchanged, or better. Objective results on MR imaging were classified as worse, no change, minor improvement (<50% decrease in size), marked improvement (>or=50% decrease), or cure. Objective and subjective results were compared. RESULTS: Twenty-one lesions showed objective improvement on MR imaging. Of these, 10 showed minor decrease in size and 11 showed marked decrease. Eleven lesions showed no change on MR imaging. No VMs were worse or completely cured. Subjectively, 29 patients and 30 clinicians thought that lesions improved. Four of 32 (12.5%) patients suffered minor transient complications. CONCLUSIONS: Percutaneous sclerotherapy by using bleomycin is a safe technique to objectively decrease size and subjectively alleviate symptoms of facial VMs. Subjective clinical improvement is not always associated with visual size reduction on MR imaging. Minimal size reduction or partial fibrosis of the lesion may be enough to achieve subjective clinical improvement.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".