Osmotic demyelination syndrome refractory to plasmapheresis treated with botulinum toxin injections: A case report and review of the literature
Bibliographic record
Abstract
This case study presents a method for treating osmotic demyelination syndrome (ODS), a rare complication resulting from the rapid correction of severe chronic hyponatremia. The report highlights the use of intramuscular botulinum toxin injections as a treatment for spasticity in ODS. The case describes a 40-year-old male with a history of panhypopituitarism, presenting with acute encephalopathy, nausea, and vomiting. Initial workup revealed severe hyponatremia, and despite initial clinical improvement with correction of the hyponatremia the patient's condition ultimately progressed to flaccid quadriparesis and spastic quadriparesis. The patient received intravenous immunoglobulin (IVIG) therapy and plasma exchange therapy (PLEX), but his symptoms worsened. The patient then received intramuscular botulinum toxin injections to target spasticity in the lower extremities, and experienced significant improvement, including reduced spasticity, and regained the ability to ambulate with assistance. This case study highlights the rarity and complexity of ODS, emphasizing the limited treatment options available. PLEX has been commonly used but many patients do not respond well to it. Intramuscular botulinum toxin injections in this case demonstrate potential benefits in managing ODS-related spasticity that is unresponsive to PLEX alone.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".