Demographic, Morphological, and Clinical Characteristics of 1289 Patients With Brain Arteriovenous Malformation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The purpose of this study was to assess demographic, clinical, and morphological characteristics of patients with brain arteriovenous malformations (AVMs). METHODS: Prospectively collected data of 1289 consecutive AVM patients from 3 independent databases (1 multicenter [Berlin/Paris/Middle and Far East, n=662] and 2 single centers [New York, n=337, and Toronto, n=290]) were analyzed. The variables assessed were age at diagnosis, sex, AVM size, AVM drainage pattern, AVM location in functionally important brain areas ("eloquence"), and type of presentation (hemorrhage, seizure, chronic headache, or focal neurologic deficit). Comparisons were made by ANOVA, contingency tables, and log-linear models. RESULTS: Overall, mean age at diagnosis was 31.2 years (95% CI 30.2 to 32.2 years), and 45% of the patients were female (95% CI 42% to 47%). AVM maximum diameter was <3 cm in 38% (95% CI 35% to 41%). Deep venous drainage was present in 55% (95% CI 52% to 59%). An eloquent AVM location was described in 71% (95% CI 69% to 74%). AVM hemorrhage occurred in 53% (95% CI 51% to 56%). Generalized or focal seizures were described in 30% (95% CI 27% to 33%) and 10% (95% CI 8% to 12%), respectively. Chronic headache was recorded in 14% (95% CI 12% to 16%). Persistent neurological deficits were found in 7% (95% CI 6% to 9%), and progressive neurological deficits in 5% (95% CI 4% to 6%). Significant differences between centers were found for age (P<0.001), sex (P=0.04), eloquence (P=0.04), size (P<0.001), hemorrhage (P=0.006), persistent neurological deficit (P<0.001), and reversible neurological deficit (P=0.013). The intercenter difference found for hemorrhage frequency did not remain after adjustment for AVM size. CONCLUSIONS: Baseline characteristics differed considerably between centers. The differences found in patient age and AVM size may be explained by center-specific referral patterns and the influence of access to treatment resources, whereas those found for other characteristics may be attributable to center-specific definitions. Analysis of natural history data from tertiary referral center databases may be improved by consistent definitions applicable to the entire population of AVM patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".