The “Little AVM”
Bibliographic record
Abstract
BACKGROUND: Arteriovenous malformations (AVMs) are high-flow lesions with abnormal connections between arteries and veins without an intervening capillary bed. Infrequently, the radiographic diagnosis of a vascular lesion will not support the clinical diagnosis of an AVM. These "discrepant" lesions are not adequately captured within the current classification system and represent a treatment dilemma. The purpose of this study is to review our center's experience with vascular malformations where incongruity in a patient's clinical and radiographic presentation produces a diagnostic and therapeutic challenge. METHODS: A retrospective chart review of patients with atypical AVM pre sen ta tions was performed. Parameters reviewed included patient history and demogra phics, clinical presentation, radiological imaging, and treatment modalities. RESULTS: Over a 15-year period, we identified 7 cases of vascular malformations with discrepant clinical and radiological findings concerning flow characteristics. All patients were treated based on their radiological diagnosis and most were managed with sclerotherapy. No lesions evolved into a high-flow process, and there was no recurrence at a minimum of 24 months of follow-up. CONCLUSIONS: We have identified and described a unique subcategory of vascular malformations that have clinical features of high-flow malformations but radiological features of low-flow malformations. These lesions behave like low-flow malformations and should be treated as such. We propose that complex vascular malformations are best evaluated by both clinical and specialized diagnostic radiological means; the radiologic diagnoses should supplant what is found clinically, and ultimately treatment should be preferentially based on a radiological diagnosis.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".