Cerebral Arteriovenous Malformations in Pediatric Patients with Hereditary Hemorrhagic Telangiectasia: Re-evaluating Appearance, Bleeding Risk, and Treatment Necessity in a Selective Meta-analysis
Bibliographic record
Abstract
Abstract Pediatric patients suffering from cerebral nidal arteriovenous malformations are a unique population due to the rare occurrence of the disease. Diagnosis of hereditary hemorrhagic telangiectasia (HHT) in children is a rare event and mainly restricted to coincidental detection from screening of possibly afflicted family members. In patients with HHT, it is well known that the incidence of cerebral vascular malformations (CVMs) is higher than expected when compared with the nonafflicted population. Even though CVMs comprise a variety of different distinct anatomical and radiographic entities (e.g., capillary telangiectasia, nidal arteriovenous malformation [AVM], cavernous malformation, dural or pial as well as mixed fistula, and vein of Galen malformation), they are mostly summarized and analyzed all in one category due to the low number of individual cases identified in single centers. Nevertheless, the best treatment algorithm will likely vary significantly between different CVMs as does the clinical presentation and the natural course of the CVM. It is therefore the objective of this article to focus on nidal AVMs in pediatric patients suffering from HHT. To this end, we performed a systematic selective literature research to estimate incidence, clinical and radiological appearance, as well as classification according to established grading system, and to evaluate the necessity of treatment of these lesions in light of their respective outcomes. Our line of arguments explains why we recommend to follow these lesions expectantly and suggest to keep pediatric patients under surveillance with sequential scans until they reach adulthood.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.016 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".