Susac's Syndrome
Bibliographic record
Abstract
A 40-year-old woman with no significant previous medical history presented with a three month history of ataxia, confusion, memory difficulties, and headaches. Physical examination revealed numbness in the left hand, but was otherwise unremarkable. Magnetic resonance imaging fluid-attenuated inversion recovery (MRI FLAIR) images demonstrated multiple small white matter hyperintensities, including lesions involving the corpus callosum. There were also deep gray nuclei lesions (Figure 1). The corpus callosum lesions involved the central fibers (Figure 2). Post gadolinium T1 images demonstrated enhancement of some of the lesions as well as extensive perivascular and leptomeningeal enhancement (Figure 3). Extensive infectious serology, autoimmune panel, and paraneoplastic antibodies were negative. Lumbar puncture revealed elevated protein (1116 mg/L), but was otherwise normal. Brain biopsy indicated no apparent pathology. The patient was tentatively diagnosed with acute encephalopathy and treated with high dose steroids seven days after presentation. She was subsequently discharged and was sent for rehabilitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".