Treatment of Neurodegenerative Ataxias With Intravenous Immune Globulin
Bibliographic record
Abstract
Background: Neurodegenerative ataxias, including spinocerebellar ataxias (SCAs), are progressive diseases without effective treatment. There is preclinical evidence that inflammation may contribute to neuronal injury in several neurodegenerative ataxias. Intravenous immune globulin (IVIG) is a therapeutic modality that is used as treatment of several autoimmune and inflammatory disorders. Methods: The primary objective of this open-label pilot study was to assess the effect of IVIG on neurodegenerative ataxias as measured by total scale for the assessment and rating of ataxia (SARA) score. Three patients received IVIG (2 g/kg of body weight, divided over 5 days) once monthly for 3 months, and were evaluated before the first infusion, 2 weeks after each infusion, as well as 28 and 56 days following the final course of treatment. Secondary measures included SARA subsection scores and gait assessment using the GAITRite ® Walkway System. Another SCA 3 patient at a different site was treated with six monthly courses of IVIG, and assessed with the SARA score. Results: Three out of five patients completed the open-label study (SCA 3, a neurodegenerative ataxia associated with an aprataxin genetic variant, and late onset cerebellar ataxia (LOCA) patient). All three patients demonstrated improvement in the SARA score following IVIG. SARA total scores improved by 30-50% and gait sub-scores improved by 20-50% after the last course of IVIG. Clinical improvements attenuated approximately 2 months following the last infusion. The SCA 3 patient treated at another site with a longer protocol had even greater improvement in the SARA score. Conclusions: IVIG may have therapeutic efficacy in neurodegen erative ataxias, including SCA and LOCA.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".