Use of intravenous immunoglobulin for treatment of neurologic conditions: a systematic review
Bibliographic record
Abstract
BACKGROUND: Given the increasing use of intravenous immunoglobulin (IVIG) for various neurologic conditions and uncertainty pertaining to its benefits and harms, a systematic review was conducted of randomized controlled trials (RCTs) evaluating IVIG for all neurologic indications for which there was at least one published trial. STUDY DESIGN AND METHODS: For this systematic review, a systematic search strategy was applied to MEDLINE (1966-June 2003) and the Cochrane Register of Controlled Trials (June 2003) to identify potentially eligible RCTs comparing IVIG to placebo or an active control. All dosage regimens were considered. Abstracts were excluded, and no restriction was placed on language of publication. Two investigators independently performed data extraction with a standardized form. Measures of effect were calculated for each trial independently, and studies were pooled based on clinical and methodologic judgment as to its appropriateness. Where pooling of trials was inappropriate, a qualitative discussion of findings is provided. RESULTS AND CONCLUSIONS: Thirty-seven trials representing 14 conditions were identified. IVIG is more effective than placebo for treatment of relapsing-remitting multiple sclerosis and idiopathic chronic inflammatory demyelinating polyneuropathy. There is also potential benefit for treatment of multifocal motor neuropathy, myasthenia gravis, dermatomyositis, stiff-person syndrome, and Lambert-Eaton myasthenic syndrome. There was insufficient evidence to determine whether IVIG therapy was more effective than plasma exchange for Guillain-Barré syndrome. There was also insufficient evidence regarding paraprotein-associated polyneuropathy. No evidence of benefit was observed for secondary progressive multiple sclerosis or inclusion body myositis.
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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".