Endovascular Management of Cerebral Arteriovenous Malformations in Pregnancy: Two Case Reports and a Review of the Literature
Bibliographic record
Abstract
Cerebral arteriovenous malformations are rare, congenital lesions that affect the vasculature of the brain and have the possibility of rupturing. The presentation of a cerebral arteriovenous malformations in a pregnant woman warrants an even greater level of concern due to the maternal physiological changes, which may affect the structural integrity of the arteriovenous malformations and lead to hemorrhaging. A proper plan of action should be deployed to successfully treat pregnant women with cerebral arteriovenous malformations. In all cases, both the mother’s and fetus’s well being must be taken into consideration. Two cases and the current management options for cerebral arteriovenous malformations in pregnancy are presented. Online databases, such as PubMed provided by the United States National Library of Medicine at the National Institutes of Health, were searched for references to convey endovascular management options for cerebral arteriovenous malformations in pregnancy. Additional references were obtained from articles that were reviewed by the authors. The first case is of a 27-year-old pregnant woman with a right-sided periventricular intraparenchymal hemorrhage. She was treated with an external ventriculostomy drain. The second case is of a 26-year-old pregnant woman with a prior stage 2 embolization of a left parietal arteriovenous malformation. She did not show evidence of intracranial hemorrhage and was treated with further embolization regimens. A multifaceted approach, tailored to the individual patient may be necessary for cerebral arteriovenous malformations in pregnancy. Successful therapeutic strategies often involve close collaboration with a group of obstetricians, neuroradiologists, anesthesiologists, and neurosurgeons. doi: http://dx.doi.org/10.4021/jnr148w
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".