Resolution of Bipolar II and Panic Disorders Following Subarachnoid Hemorrhage
Bibliographic record
Abstract
Cerebral infarction producing psychiatric disorders such as depression and mania is a recognized phenomenon. However, resolution of affective disorders following stroke has not been previously reported. We describe the case of a 53-year-old woman with a 25-year history of treatment-resistant bipolar II and panic disorders. At the age of 46, she experienced a subarachnoid hemorrhage with secondary vasospasm that resulted in a stroke. Shortly following the hemorrhage, the patient experienced a complete remission of both psychiatric illnesses that has been sustained for 7 years. Initial computed tomography (CT) and angiography studies revealed subarachnoid hemorrhage with intraventricular extension, communicating hydrocephalus, and aneurysms of the left posterior communicating artery and the right anterior cerebral artery. Following clipping of the left internal posterior communicating artery aneurysm, the patient developed vasospasm with further stroke symptoms. A subsequent CT scan showed a fully developed ischemic infarct in the left temporoparietal region that was confirmed by follow-up magnetic resonance imaging (MRI). We present a 7-year follow-up with complete psychiatric interview, chart review, and MRI. The present case demonstrates the importance of continued efforts to localize neural circuits involved in the pathogenesis and maintenance of affective disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".