New Frontiers in Subcutaneous Immunoglobulin Treatment
Bibliographic record
Abstract
Subcutaneous immunoglobulin (SCIG) treatment provides stable serum immunoglobulin G (IgG) levels, is associated with fewer systemic adverse events than intravenous immunoglobulin (IVIG) treatment, and offers the convenience of home therapy. In clinical practice, IVIG is still used preferentially for initiation of treatment in newly diagnosed patients with primary immunodeficiency (PI) and for immunomodulatory therapy, such as treatment of peripheral neuropathies, when high doses are believed to be necessary. The authors discuss recent experience in using SCIG in place of IVIG in these settings. SCIG has been successfully used for initiation of therapy in previously untreated PI patients. Seventeen of 18 PI patients achieved serum IgG levels ≥5 g/L after the loading phase. Daily treatment was well tolerated and provided opportunities for patient/parent training in self-infusion. SCIG has been used for maintenance therapy in multifocal motor neuropathy (MMN) in three recent clinical trials, with good efficacy and tolerability results. Seven of eight MMN patients maintained serum IgG levels of 14-22 g/L with a mean dose of 272 mg/kg/week, had stable muscle strength, and felt comfortable with self-administration. Four patients with polymyositis or dermatomyositis achieved improvement in serum creatine kinase levels and muscle strength with SCIG therapy. Recent experience with SCIG suggests that traditional concepts of immunoglobulin therapy may be challenged to increase available therapy options. SCIG can be used to achieve high IgG levels within several days in untreated PI patients and to maintain high serum levels, as shown in patients with MMN.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".