Brain Arteriovenous Malformation Multiplicity Predicts the Diagnosis of Hereditary Hemorrhagic Telangiectasia
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The purpose of this study was to quantitatively estimate the relationship between multiplicity of brain arteriovenous malformations (bAVMs) and the diagnosis of hereditary hemorrhagic telangiectasia (HHT). METHODS: We combined databases from 2 large North American bAVM referral centers, including demographics, clinical presentation, and angiographic characteristics, and compared patients with HHT with non-HHT patients. Logistic regression analysis was performed to quantify the association between bAVM multiplicity and odds of HHT diagnosis. Sensitivity, specificity, positive and negative predictive value, and positive and negative likelihood ratios were calculated to determine accuracy of bAVM multiplicity for screening HHT. RESULTS: Prevalence of HHT was 2.8% in the combined group. bAVM multiplicity was present in 39% of patients with HHT and was highly associated with diagnosis of HHT in univariate (OR, 83; 95% CI, 40-173; P<0.0001) and multivariable (OR, 86; 95% CI, 38-195; P<0.001) models adjusting for age at presentation (P=0.013), symptomatic presentation (P=0.029), and cohort site (P=0.021). bAVM multiplicity alone was associated with high specificity (99.2%; 95% CI, 98.7%-99.6%) and negative predictive value (98.3%; 95% CI, 97.6%-98.8%) and low sensitivity (39.3%; 95% CI, 26.5%-53.2%) and positive predictive value (59.5%; 95% CI, 42.1%-75.2%). Positive and negative likelihood ratio was 51 and 0.61, respectively, for diagnosis of HHT. HHT bAVMs were also more often smaller in size (<3 cm), noneloquent in location, and associated with superficial venous drainage compared with non-HHT bAVMs. CONCLUSIONS: Multiplicity of bAVMs is highly predictive of the diagnosis of HHT. The presence of multiple bAVMs should alert the clinician to the high probability of HHT and lead to comprehensive investigation for this diagnosis.
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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) | 0.000 | 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) | 0.000 | 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".