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Record W1971690756 · doi:10.1002/mus.23236

Predictors of response to immunomodulation in patients with myasthenia gravis

2011· article· en· W1971690756 on OpenAlexaff
Hans Katzberg, Carolina Barnett, Vera Bril

Bibliographic record

VenueMuscle & Nerve · 2011
Typearticle
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMyasthenia gravisPlasmapheresisMedicineLogistic regressionInternal medicineOdds ratioElectromyographyPlaceboMultivariate analysisImmunologyAntibodyPathologyPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

INTRODUCTION: Factors determining response to intravenous immunoglobulin (IVIg) and plasmapheresis in myasthenia gravis (MG) have not been evaluated systematically. METHODS: This study included patients treated with IVIg (n = 63) or plasmapheresis (n = 42) from two trials evaluating IVIg vs. placebo or plasmapheresis in MG. Response was defined as improvement in the quantitative myasthenia gravis score (QMGS) of ≥3.5 points at day 14. Baseline clinical, electrophysiological, and immunological factors were analyzed as predictors. RESULTS: Baseline QMGS, acetylcholine receptor antibody (AChRAb) positivity, single-fiber electromyography (SFEMG) jitter, and percent abnormal pairs and percent blocking pairs were higher in responders than in non-responders. Using multivariate logistic regression, the odds ratio for response was 13.0 (1.01-381.5) in QMGS 11-17 and 15.3 (1.34-414.3) in QMGS >17 compared with QMGS <11. CONCLUSIONS: Baseline QMGS, AChRAb positivity, and SFEMG parameters were more abnormal in patients who responded to treatment. Using multivariate regression, baseline QMGS remained as the only significant independent predictor of response.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.210
Teacher spread0.198 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations25
Published2011
Admission routes1
Has abstractyes

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