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IV immunoglobulin in patients with myasthenia gravis

2007· article· en· W1971022805 on OpenAlexafffund
Lorne Zinman, Eduardo Ng, Vera Bril

Bibliographic record

VenueNeurology · 2007
Typearticle
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsSunnybrook Health Science Centre
FundersUniversity Health Network
KeywordsMyasthenia gravisMedicineWeaknessRandomized controlled trialPlaceboNeuromuscular diseaseInternal medicineAntibodyMuscle weaknessDiseaseSurgeryImmunologyPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: We aimed to determine the effectiveness of IV immunoglobulin (IVIG) in the treatment of patients with myasthenia gravis (MG) and worsening weakness in a randomized, placebo-controlled, masked study. METHODS: Fifty-one patients with worsening weakness due to MG were randomized to infusion with 2 g/kg of IVIG or an equivalent volume of IV dextrose 5% in water. The Quantitative Myasthenia Gravis (QMG) Score for Disease Severity, a validated clinical composite scale, was calculated by a masked observer at baseline and days 14 and 28. RESULTS: In IVIG-treated patients, a clinically meaningful improvement in QMG Score for Disease Severity was observed at day 14 and persisted at day 28. The greatest improvement occurred in patients with more severe disease as defined by a QMG Score for Disease Severity greater than 10.5. CONCLUSION: This study provides level 1 evidence for the effectiveness of IV immunoglobulin in patients with worsening weakness due to myasthenia gravis.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.005
GPT teacher head0.219
Teacher spread0.214 · 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
GenreEmpirical

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

Citations268
Published2007
Admission routes2
Has abstractyes

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