P.077 Mixed autoimmune hemolytic anemia: an unusual cause of ischemic stroke and extensive cerebral microbleeds
Bibliographic record
Abstract
Background: Mixed autoimmune hemolytic anemia (mAIHA) is a rare autoimmune disorder that results in hemolysis with thrombotic complications like ischemic stroke. This is the first case report of cerebral microbleeds secondary to mAIHA. Methods: A literature review of mAIHA and cerebral microbleeds was conducted using the PubMed and Ovid MEDLINE databases from 1980 to 2021. Results: A 76 year old male with congenital deafness and rheumatoid arthritis presented with diffuse livedo reticularis and abdominal pain. He had fulminant hemolysis with new neurologic deficits and altered mental status. CT/CTA of the head and neck were unremarkable. MR brain revealed extensive cerebral microbleeds and multi-territory ischemic strokes. He was diagnosed with mAIHA, started on pulse methylprednisolone, and had no further microbleeds on follow-up MRI. From his clinical picture, common causes of cerebral microbleeds were ruled out such as cerebral amyloid angiopathy and hypertension. The pathogenesis of his microbleeds may be from concomitant severe hypoxia or a prothrombotic state, both previously reported in the literature. Conclusions: This is the first case report of extensive cerebral microbleeds secondary to mAIHA. When a patient develops acute neurologic deficits in the context of mAIHA, extensive cerebral microbleeds may be present possibly due to concomitant severe hypoxia versus a prothrombotic state.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".