Facial Venous Malformations Are Associated with Cerebral Developmental Venous Anomalies
Bibliographic record
Abstract
<h3>BACKGROUND AND PURPOSE:</h3> A number of studies have demonstrated the existence of segmental vascular disorders affecting soft tissues of the head and neck along with the intracranial vasculature. The purpose of this study was to determine whether there is an association between cerebral developmental venous anomalies and venous malformations of the face, head, and neck. <h3>MATERIALS AND METHODS:</h3> A consecutive series of patients with head and neck venous malformations who underwent MR imaging of the brain with postcontrast T1- or T2*-weighted imaging were included. Developmental venous anomaly prevalence in this patient population was compared with an age- and sex-matched control group without venous malformations at a ratio of 1:2. All images were interpreted by 2 neuroradiologists. Data were collected on venous malformation location, developmental venous anomaly location, developmental venous anomaly drainage pattern, and metameric location of venous malformations and developmental venous anomalies. Categoric variables were compared using χ<sup>2</sup> tests. <h3>RESULTS:</h3> Forty-two patients with venous malformations were included. The mean age was 38.1 ± 11.1 years, and 78.6% of patients were female. The prevalence of developmental venous anomalies in this patient population was 28.6%. The control population of 84 patients had a mean age of 40.0 ± 5.9 years, and 78.6% of patients were female. The prevalence of developmental venous anomalies in this patient population was 9.5% (<i>P</i> = .01). In 83.3% of cases, developmental venous anomalies were ipsilateral to the venous malformation, and in 75% of cases, they involved the same metamere. <h3>CONCLUSIONS:</h3> Our case-control study demonstrated a significant association between brain developmental venous anomalies and superficial venous malformations. These findings suggest that there may be a similar pathophysiologic origin for these 2 entities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".