Subcutaneous versus intravenous immunoglobulin for chronic autoimmune neuropathies: A meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: High-dose intravenous immunoglobulin (IVIg) is an evidence-based treatment for multifocal motor neuropathy (MMN) and chronic inflammatory demyelinating polyneuropathy (CIDP). Recently, subcutaneous immunoglobulin (SC-Ig) has received increasing attention. METHODS: We performed a meta-analysis of reports of efficacy and safety of SC-Ig versus IVIg for inflammatory demyelinating polyneuropathies. RESULTS: A total of 8 studies comprising 138 patients (50 with MMN and 88 with chronic CIDP) were included in the meta-analysis. There were no significant differences in muscle strength outcomes in MMN and CIDP with Sc-Ig (MMN: effect size [ES] = 0.65, 95% confidence interval [CI] = -0.31-1.61; CIDP: ES = 0.84, 95% CI = -0.01-1.69). Additionally SC-Ig had a 28% reduction in relative risk (RR) of moderate and/or systemic adverse effects (95% CI = 0.11-0.76). CONCLUSIONS: The efficacy of SC-Ig is similar to IVIg for CIDP and MMN and has a significant safety profile. Muscle Nerve 55: 802-809, 2017.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".