Soft-Tissue Venous Malformations in Adult Patients: Imaging and Therapeutic Issues
Bibliographic record
Abstract
Venous malformations are the most common vascular malformations. However, confusion with respect to terminology and imaging guidelines continues to result in improper diagnosis and treatment. An appropriate classification scheme for vascular anomalies is important to avoid the use of false generic terms. Adequate imaging in association with clinical findings is crucial to establishing the correct diagnosis. Doppler ultrasonography should be the initial imaging modality and demonstrates absence of flow or low-velocity venous flow. Computed tomography and magnetic resonance (MR) imaging are used primarily for pretreatment evaluation of lesion extension. These lesions are usually hypointense on T1-weighted MR images and markedly hyperintense on T2-weighted images with variable gadolinium enhancement. Direct phlebography helps confirm the diagnosis and exclude other soft-tissue tumors. Three distinct phlebographic patterns (cavitary, spongy, dysmorphic) have been identified. In most cases, conservative treatment is recommended. Sclerotherapy with or without surgery is useful in cases of functional impairment or significant aesthetic prejudice, even if recurrences are frequent. Direct phlebography is performed when a more detailed assessment of the vascular pattern is needed or as part of sclerotherapy. Use of the appropriate imaging technique is critical in establishing the diagnosis, evaluating extension, and planning appropriate treatment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".