Immunoglobulin versus Plasmapheresis in treatment of Myasthenia Gravis: a systematic review
Bibliographic record
Abstract
Introduction: Myasthenia Gravis (MG) is an autoimmune and neuromuscular disease. The treatment of immunomodulation consists of intravenous immunoglobulin (IVIg), immunoadsorption (IA), plasmapheresis (PLEX) or double filtration plasmapheresis (DFPP). This systematic review aims to compare therapy modalities in MG crisis. Methods: The studies were identified through research in electronic databases and analyzed individually to clarify their methodological quality (through the Jadad and Newcastle Ottawa scale). Clinical trials randomized or not, and retrospective studies were included. The review included 1,983 patients in nine studies, the result analysis groups were divided into: IVIg x PLEX in the crises; IVIg x PLEX in the pre-thymectomy treatment phase and IVIg x DFPP in the myasthenic crisis. The evaluated outcomes were clinical improvement, adverse effects and length of hospital stay. Results: Immunomodulatory therapy when applied prior to thymectomy was shown to be effective in reducing symptoms and post-thymectomy hospitalization, with IVIg slightly higher, while PLEX showed more side effects. Therapy during crises in both interventions proved to be effective after the 14th start of treatment, in addition to IVIg being slightly superior. Treatment with IVIg was also economically favorable, due to the lower need for hospitalizations. IVIg proved to be inferior to therapy with DFPP and IA, mainly in reducing the need for hospitalization. Conclusion: It is concluded that IVIg therapy is a good therapeutic option in cases of crisis and when available, therapies with DFPP and IA should be the choices, aiming at less complications.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".