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Record W2482400956 · doi:10.17925/enr.2009.04.01.72

Chronic Inflammatory Demyelinating Polyradiculoneuropathy - An Overview of Intravenous Immunoglobulin Therapy

2009· article· en· W2482400956 on OpenAlexaff
Vera Bril

Bibliographic record

VenueEuropean Neurological Review · 2009
Typearticle
Languageen
FieldMedicine
TopicPeripheral Neuropathies and Disorders
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicinePolyradiculoneuropathyPlaceboRegimenIntravenous ImmunoglobulinsMaintenance therapyInternal medicineAntibodyPediatricsImmunologyChemotherapyGuillain-Barre syndromePathologyAlternative medicine

Abstract

fetched live from OpenAlex

Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is a significant source of disability, and early diagnosis and immunomodulatory therapy administration are critical to minimise disease progression and axonal degeneration. Intravenous immunoglobulin (IVIg) therapy is considered to be a first-line treatment for CIDP. Comparative short- and long-term data of IVIg versus corticosteroids in CIDP patients are limited. Of the five published placebo-controlled studies in CIDP, four reported only on short-term improvements in disability (≤6 weeks). However, the IGIV CIDP Efficacy (ICE) study, the largest randomised, placebo-controlled CIDP study published to date (n=117), reported significant improvements in disability, functional impairment and quality of life with IVIg (Gamunex®) 1g/kg maintenance therapy every three weeks for up to 48 weeks. Furthermore, long-term IVIg administration was safe and well tolerated, particularly given the short duration of the infusions. Data suggest that a long-term scheduled maintenance regimen of IVIg in appropriate patients may provide substantial benefit and reduce the risk of CIDP relapse.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.053
GPT teacher head0.316
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2009
Admission routes1
Has abstractyes

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