Hemorrhagic Moyamoya Disease during Pregnancy
Bibliographic record
Abstract
BACKGROUND: Intracranial hemorrhage in pregnant patients with Moyamoya disease is rare. We review the case of one such patient who presented with pre-eclampsia and a catastrophic intracerebral hemorrhage in order to highlight the associated management difficulties. METHODS: A case of a pregnant (31 weeks) female brought to the emergency department with hypertension and a progressive decrease in her level of consciousness is presented. She rapidly developed a dilated right pupil and left extensor posturing. A CT scan of her head showed a large putamenal intracerebral hemorrhage. She was intubated, ventilated and given intravenous mannitol and magnesium sulfate. She underwent a simultaneous craniotomy and Cesarean section. Post-operatively the patient's ICP and jugular venous saturation were monitored in the intensive care unit. RESULTS: The patient delivered a 1185 g infant who did well. The patient's ICP was well controlled until the tenth post-operative day when she developed malignant brain edema and died. CONCLUSION: This case highlights three important points. First, simultaneous craniotomy and Cesarean section can be performed. Second, intraoperative control of bleeding Moyamoya vessels is described. Third, the difficult post-operative management of these cases is highlighted. The literature regarding Moyamoya disease and pregnancy is reviewed and some recommendations for the management of this rare but potentially deadly condition are presented.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".