Angiographic and clinical characteristics of patients with cerebral arteriovenous malformations associated with hereditary hemorrhagic telangiectasia.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cerebral arteriovenous malformations (AVMs) are occasionally associated with hereditary hemorrhagic telangiectasia (HHT), which is characterized by the presence of multiple mucocutaneous telangiectasia, epistaxis, and familial inheritance. We analyzed the angiographic and clinical characteristics of patients with cerebral AVMs related to HHT. METHODS: Among 638 patients with cerebral AVMs, we identified 14 patients with HHT. The AVMs were classified as those with nidi of 1 cm or less (micro AVMs), those with nidi between 1 and 3 cm (small AVMs), and those of the fistulous type (arteriovenous fistulas [AVFs]). RESULTS: A total of 28 AVMs were found; seven of 14 patients had multiple AVMs. The 28 AVMs were categorized as 12 micro AVMs, eight small AVMs, and eight AVFs. All except one micro AVM were asymptomatic, whereas all small AVMs were symptomatic. Three of eight AVFs were asymptomatic. All 28 AVMs were located on the cortex. All micro AVMs and AVFs had single feeders and single draining veins, whereas the small AVMs had multiple feeders in all lesions and single draining veins in six of eight lesions. CONCLUSION: Multiple, cortical, micro AVMs or AVFs harboring single feeding arteries and single draining veins should raise clinical suspicion of HHT-related AVMs.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".