E.08 Subcutaneous vs. intravenous immunoglobulins for chronic inflammatory demyelinating polyneuropathy and multifocal motor neuropathy
Bibliographic record
Abstract
Background: Background: High-dose intravenous immunoglobulin (IV-Ig) is an evidence-based treatment for chronic inflammatory demyelinating polyneuropathy (CIDP) and multifocal motor neuropathy (MMN). Recently, subcutaneous Ig (SC-Ig) has received increasing attention. We performed a meta-analysis to assess the efficacy of SC-Ig versus IV-Ig. Methods: Methods: We searched PubMed, Embase, and Scopus from January, 1990 to December, 2015 for publications comparing IV-Ig vs. SC-Ig in patients with CIDP or MMN. We performed fixed-effects meta-analyses for strength changes as measured by the Medical Research Council sum score changes (MRC-SS). Results: Results: A total of 8 studies comprising 138 patients (88 with CIDP and 50 with MMN) were included in the meta-analysis. Considering the total population the use of SC-Ig showed slightly better results for MRC-SS (ES=-1.78, 95%CI=-3.45 to -0.11, I2<0.001%). However, when CIDP and MMN were compared separately, there were no differences between treatments (CIDP: ES=-0.28, 95%CI=-0.57 to 0.02, I2<0.001%; MMN: ES=-0.34, 95%CI=-3.99 to 3.31, I2<0.001%). Conclusions: Conclusions: We found comparable efficacy between SC and IV-Ig administrations for CIDP and MMN. These results suggest that SC-Ig is a suitable alternative treatment method, especially when other situations (e.g. convenience, safety profile) warrant its use. Further studies are needed to explore the efficacy of SC-Ig for CIDP and 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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.031 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".