P.091 Intracerebral hemorrhage secondary to multiple myeloma: a systematic review
Bibliographic record
Abstract
Background: Multiple myeloma (MM) as a cause of spontaneous intracranial hemorrhage has not been well established. Methods: We report a patient who developed a spontaneous intracerebral hemorrhage secondary to MM and conduct a systematic review of the literature. In addition, we discuss the underlying pathophysiology. Results: A 67-year-old relatively healthy female with a recent history of low back pain presented with an altered level of consciousness and left sided hemiplegia. CT demonstrated a large right temporal intracerebral hemorrhage. CT angiogram ruled out a vascular abnormality; however, multiple abnormal bony lesions were incidentally noted. Other causes for intracranial bleed were ruled out. She underwent a craniotomy for hematoma evacuation. Intra-operatively, the skull was noted to be abnormal and hematoma was not associated with a mass lesion. In addition, serum and urine electrophoresis were found to be positive for monoclonal free kappa light chains. Subsequent bone biopsy confirmed the diagnosis of MM. Our literature search identified 2 reported cases of spontaneous subdural hematomas and 2 patients with spontaneous intracerebral hematomas secondary to MM. Moreover, only 4 reports in the literature document intracranial hemorrhage secondary to a mass developed from MM. Conclusions: Multiple myeloma is perhaps an under-reported possible cause for spontaneous intracerebral hematoma.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".