Brain Arteriovenous Malformation and Its Implication in Forensic Pathology<sup>*</sup>
Bibliographic record
Abstract
Despite the fact that brain arteriovenous malformations (BAVMs) are a possible cause of sudden and unexplained death, very few papers have discussed their importance in the forensic context. BAVMs consist of tangled masses of tortuous arteries and veins devoid of intervening capillaries that frequently extend from brain parenchyma into the subarachnoid space. Apart from BAVMs, three major groups of vascular malformations of the brain are known: cavernous hemangioma, venous angioma, and capillary telangiectasia. BAVMs and cavernous hemangioma often cause hemorrhages, while venous angioma and capillary telangiectasia are typically asymptomatic. Presented here is the case of a 14-year-old girl who died from a ruptured BAVM. The present case is a reminder that the forensic pathologist should be able to recognize BAVMs and to differentiate it from other types of vascular malformations. Although rare, it is a cause of sudden death not to be overlooked, especially in children.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".