Intravenous immunoglobulin for chronic inflammatory demyelinating polyradiculoneuropathy: the ICE trial
Bibliographic record
Abstract
Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is a potentially disabling autoimmune disease causing progressive or relapsing-remitting weakness with or without sensory loss. Previous small trials demonstrated short-term benefit from intravenous immunoglobulin (IVIg), and international guidelines recommend IVIg as an option. However, evidence had been insufficient to persuade authorities to approve IVIg for use in CIDP. This article aims to review the Immune Globulin Intravenous CIDP Efficacy (ICE) trial, which was a randomized, double-blind, placebo-controlled, response-conditional crossover trial of Gamunex (intravenous immunoglobulin, 10% caprylate/chromatography purified). With 117 participants, it is the largest treatment trial ever conducted in CIDP. The results showed unequivocal short- and long-term benefit from IVIg in confirmation of previous reports. The trial also showed for the first time that continued IVIg infusion 1 g/kg every 3 weeks protected participants from relapse. Adverse events were mostly mild and serious adverse events were not more common with IVIg than with placebo. The results persuaded the US FDA and Health Canada to approve Gamunex for use in CIDP.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".