Subcutaneous immunoglobulin for patients with idiopathic inflammatory myopathies: a real-world, single-centre experience
Bibliographic record
Abstract
OBJECTIVES: Idiopathic inflammatory myopathies (IIMs) are heterogeneous diseases characterized by skeletal muscle inflammation associated with cutaneous, pulmonary and/or other visceral organ involvement. IVIG has been recommended as an adjunct therapy for IIM patients refractory to conventional therapy. However, IVIG has high resource needs and increased risk of adverse reactions. Subcutaneous immunoglobulin (SCIG) therapy has been used as an alternative to IVIG in primary immunodeficiencies and neuroinflammatory disorders. We assessed the satisfaction, patient preference and effectiveness in IIM patients who transitioned from IVIG to SCIG. METHODS: We retrospectively reviewed consecutive 20 patients with IIM who were transitioned from IVIG to SCIG therapy for >12 months. Patient preference between IVIG and SCIG was surveyed using a questionnaire previously used in studies of neuroinflammatory conditions. In addition, disease flares, changes in immunosuppression, cumulative prednisone doses and global disease activity were evaluated using the Myositis Intention to Treat Index (MITAX) 12 months pre- and post-SCIG initiation. RESULTS: Most patients (78.9%) preferred SCIG over IVIG and preferred home-based therapies to hospital-based therapies. There was no significant difference in global disease activity (MITAX 3.31 vs 3.02) or in cumulative steroid doses 12 months pre- or post-SCIG initiation. Three patients experienced disease flares, five escalated in immunosuppression, while four patients deescalated in immunosuppressive medications. CONCLUSIONS: SCIG is preferred by most patients over IVIG without a substantial increased disease activity or need for additional CS. Future cost-effectiveness studies may provide an additional rationale for utilizing SCIG over IVIG for maintenance therapy for IIM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".