Outcomes of arteriovenous malformation patients with multiple versus single feeders: A multicenter retrospective study with propensity-score matching
Bibliographic record
Abstract
Abstract Introduction The impact of multiple feeding arteries on clinical outcomes of cerebral arteriovenous malformations (AVMs) is not well understood. This study aims to compare outcomes between AVMs with multiple versus single feeding arteries. Patients and Methods Data from the Multicenter International Study for Treatment of Brain AVMs (MISTA) consortium were analyzed. Propensity score matching (PSM) was used to balance cohorts. Subgroup analysis was conducted for ruptured and unruptured AVMs and different treatment options, and multivariable logistic regression was applied to assess the impact of feeding artery origin. Results Among 953 patients, 661(69.4%) had multiple feeding arteries, and 292 (30.6%) had a single feeding artery. After PSM, which included 422 matched patients (211 in each group), the differences in obliteration rates (68.7% vs 74.8%, OR 0.73, 95% CI: 0.48–1.12, p = 0.16) and symptomatic complications (15.6% vs 11.8%, OR 1.37, 95% CI: 0.78–2.41, p = 0.25) were not significant. Subgroup analysis comparing ruptured and unruptured AVMs and different treatment options showed no significant differences across all subgroups. Multivariable analysis identified PICA feeders as significantly associated with increased odds of all complications (OR 7.33, 95% CI: 2.14–25.1, p = 0.002). Discussion and Conclusion AVMs with a single feeding artery were more likely to present with rupture, but no significant differences in obliteration rates or complications were observed between the groups after PSM. These findings suggest that while the number of feeding arteries may influence the initial presentation, it does not appear to impact overall treatment success or patient prognosis. Further prospective studies are needed to confirm these findings.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".