E.3 Behind the brain’s veil: unraveling the neuroimaging mysteries of CNS Balamuthia mandrillaris
Bibliographic record
Abstract
Background: Balamuthia mandrillaris is a rare protozoan pathogen that causes severe central nervous system (CNS) infections in humans. Given the complexity and rarity of these infections, understanding the radiological features is key for early diagnosis and management. This case series aims to elucidate the spectrum of imaging findings in microbiologically confirmed Balamuthia CNS infection cases. Methods: A retrospective study analyzing imaging findings of 20 patients with confirmed Balamuthia CNS infections collected from the hospital’s archives, all of whom had positive CSF cultures and underwent gadolinium-enhanced MRI scans. Results: Patients presented with non-specific symptoms including headaches and seizures. Imaging revealed multiple intra-axial enhancing lesions with surrounding vasogenic edema, some demonstrating ring enhancement and typical imaging features of intracranial abscesses. Cerebritis, hemorrhagic infarcts and necrosis were also noted. Conclusions: CNS infections have a diverse group of causative organisms, including amoebic ones like Balamuthia, and often present with overlapping symptoms, complicating diagnosis. Accurate and timely imaging recognition, combined with CSF analysis, is essential for diagnosing and managing patients promptly, improving overall patients outcome in Balamuthia mandrillaris CNS infections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".