Reversible Posterior Leukoencephalopathy Syndrome in a Patient Treated With Ustekinumab
Bibliographic record
Abstract
BACKGROUND: Reversible posterior leukoencephalopathy syndrome (RPLS) is a rare, generally reversible neurologic syndrome that is diagnosed based on characteristic clinical and radiologic findings. OBSERVATIONS: We describe the first case of RPLS in a 65-year-old woman who underwent ustekinumab therapy for psoriasis. Approximately 2½ years after the patient began ustekinumab therapy, she experienced an acute onset of confusion, headache, nausea, vomiting, and seizures. Computed tomographic scans and magnetic resonance images of her head revealed characteristic findings, including white matter abnormalities consistent with edema in the absence of infarction. There was no evidence of vasospasm, thrombosis, or infection. Cerebrospinal fluid tests were negative for the JC virus. The patient improved clinically and was discharged 6 days after she presented to the emergency department. She made a full neurologic recovery, with a reversal of the radiologic findings. CONCLUSIONS: Reversible posterior leukoencephalopathy syndrome is an increasingly recognized neurologic disorder that has been reported with the use of systemic and biologic agents to treat moderate to severe psoriasis. Although the relationship between RPLS and ustekinumab therapy remains unclear, this case emphasizes the need for dermatologists to recognize the syndrome's signs and symptoms and to refer patients promptly for evaluation and appropriate treatment if the clinical features of RPLS are suspected.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".